Reviewer Comment Discussion
Erin M. Buchanan
2023-03-20
Libraries
library(dplyr)
library(tidyr)
library(ggplot2)
library(lme4)
library(lmerTest)
library(flextable)
library(rio)
library(ggmosaic)
library(kableExtra)
library(maps)
library(countrycode)
library(treemapify)
library(MOTE)
library(broom)
options(scipen = 10)Import Data
load("linear_exclude.Rdata")
linear_exclude <- output
load("linear_noexclude.Rdata")
linear_no <- output
load("log_exclude.Rdata")
log_exclude <- output
load("log_noexclude.Rdata")
log_no <- output
rm(output)
load("final_long_log_exclude.Rdata")
load("final_luck_log_exclude.Rdata")
lab_sheet <- import("lab_sheet_un_region_update.csv")
load("full_long_log_exclude.Rdata")Modeling Comparison
In this section, we examine two questions:
- The impact of the change in exclusion criteria: we included all CREP teams that at least were approved and excluded those who marked age as over 100 as unlikely numbers.
- The impact of dichotomization: data screening results indicated that the model was not linear, and we dichotomized the data.
- We ran all four combinations of these two variables to examine the results.
Data Screening
- In order to explain why we made the decision to split the data, we examined data screening on Model 6 with all random intercepts, covariates, and the main effect of condition. Each of these is provided below. Click view data screening to view the outputs.
- As shown in the data screening, the linear model does not meet the assumptions of normality, linearity, or homoscedasticity. Given the previous study was binary and a visual inspection of the data showed a u-shaped distribution, we choose to dichotomize the data. The logistic regression data screening shows that the assumptions of additivity and linearity of the logit for continuous predictors was met.
Linear, Pre-registered Exclusions
View data screening
# additivity
round(cov2cor(vcov(linear_no[[5]])),2)## 7 x 7 Matrix of class "dpoMatrix"
## (Intercept) compYes age gender2male education condIgnorance
## (Intercept) 1.00 -0.10 -0.08 -0.03 -0.26 -0.06
## compYes -0.10 1.00 0.15 0.00 -0.06 0.00
## age -0.08 0.15 1.00 0.00 -0.10 0.00
## gender2male -0.03 0.00 0.00 1.00 -0.01 0.00
## education -0.26 -0.06 -0.10 -0.01 1.00 0.00
## condIgnorance -0.06 0.00 0.00 0.00 0.00 1.00
## condKnowledge -0.06 0.00 0.00 0.00 0.00 0.50
## condKnowledge
## (Intercept) -0.06
## compYes 0.00
## age 0.00
## gender2male 0.00
## education 0.00
## condIgnorance 0.50
## condKnowledge 1.00# normality
standardized <- residuals(linear_no[[5]], type = "pearson")
hist(standardized) 
# linearity
{qqnorm(standardized)
abline(0,1)}
# homosc
fitted <- scale(fitted.values(linear_no[[5]]))
{plot(fitted, standardized)
abline(v = 0)
abline(h = 0)}
Linear, New Exclusions
View data screening
# additivity
round(cov2cor(vcov(linear_exclude[[5]])),2)## 7 x 7 Matrix of class "dpoMatrix"
## (Intercept) compYes age gender2male education condIgnorance
## (Intercept) 1.00 -0.10 -0.11 -0.02 -0.21 -0.05
## compYes -0.10 1.00 0.17 0.00 -0.04 0.00
## age -0.11 0.17 1.00 -0.03 -0.11 0.00
## gender2male -0.02 0.00 -0.03 1.00 0.01 0.00
## education -0.21 -0.04 -0.11 0.01 1.00 0.00
## condIgnorance -0.05 0.00 0.00 0.00 0.00 1.00
## condKnowledge -0.05 0.00 0.00 0.00 0.00 0.50
## condKnowledge
## (Intercept) -0.05
## compYes 0.00
## age 0.00
## gender2male 0.00
## education 0.00
## condIgnorance 0.50
## condKnowledge 1.00# normality
standardized <- residuals(linear_exclude[[5]], type = "pearson")
hist(standardized) 
# linearity
{qqnorm(standardized)
abline(0,1)}
# homosc
fitted <- scale(fitted.values(linear_exclude[[5]]))
{plot(fitted, standardized)
abline(v = 0)
abline(h = 0)}
Logistic, Pre-registered Exclusions
View data screening
# additivity
round(cov2cor(vcov(log_no[[6]])),2)## 7 x 7 Matrix of class "dpoMatrix"
## (Intercept) compYes age gender2male education condIgnorance
## (Intercept) 1.00 -0.06 -0.09 -0.03 -0.28 -0.05
## compYes -0.06 1.00 0.17 -0.01 -0.16 0.00
## age -0.09 0.17 1.00 -0.07 -0.11 0.00
## gender2male -0.03 -0.01 -0.07 1.00 0.01 0.00
## education -0.28 -0.16 -0.11 0.01 1.00 0.00
## condIgnorance -0.05 0.00 0.00 0.00 0.00 1.00
## condKnowledge -0.06 0.00 0.00 0.00 -0.01 0.42
## condKnowledge
## (Intercept) -0.06
## compYes 0.00
## age 0.00
## gender2male 0.00
## education -0.01
## condIgnorance 0.42
## condKnowledge 1.00# linearity logit
df <- log_no[[6]]@frame
df$probs <- predict(log_no[[6]], type = "response")
df$logit <- log(df$probs/(1-df$probs))
ggplot(df, aes(logit, age)) +
geom_point() +
theme_classic() +
geom_smooth(method = "lm") +
facet_wrap(~vignette*cond)
ggplot(df, aes(logit, education)) +
geom_point() +
theme_classic() +
geom_smooth(method = "lm") +
facet_wrap(~vignette*cond)
Logistic, New Exclusions (Paper)
View data screening
# additivity
round(cov2cor(vcov(log_exclude[[6]])),2)## 7 x 7 Matrix of class "dpoMatrix"
## (Intercept) compYes age gender2male education condIgnorance
## (Intercept) 1.00 -0.08 -0.11 -0.03 -0.24 -0.05
## compYes -0.08 1.00 0.20 0.01 -0.12 0.00
## age -0.11 0.20 1.00 -0.09 -0.11 -0.01
## gender2male -0.03 0.01 -0.09 1.00 0.03 0.01
## education -0.24 -0.12 -0.11 0.03 1.00 0.01
## condIgnorance -0.05 0.00 -0.01 0.01 0.01 1.00
## condKnowledge -0.05 0.00 0.00 -0.01 -0.01 0.43
## condKnowledge
## (Intercept) -0.05
## compYes 0.00
## age 0.00
## gender2male -0.01
## education -0.01
## condIgnorance 0.43
## condKnowledge 1.00# linearity logit
df <- log_exclude[[6]]@frame
df$probs <- predict(log_exclude[[6]], type = "response")
df$logit <- log(df$probs/(1-df$probs))
ggplot(df, aes(logit, age)) +
geom_point() +
theme_classic() +
geom_smooth(method = "lm") +
facet_wrap(~vignette*cond)
ggplot(df, aes(logit, education)) +
geom_point() +
theme_classic() +
geom_smooth(method = "lm") +
facet_wrap(~vignette*cond)
Overall Model Comparison
- Intercept only model DV ~ 1
- Intercept model with random intercept vignette
- Intercept model with random intercept vignette/participant id, no linear model, would not converge
- Intercept model with random intercept vignette/id/lab, for the linear model no participant id was used
- Random intercepts and covariates
- Random intercepts, covariates, and condition 6a) Random intercepts, covariates, and condition by vignette 6b) Random intercepts, covariates, and condition by turk
- These results are discussed below within each dependent variable;
however, the general summary is the same for each:
- Covariate models are all better (model 5 < model 4)
- Condition adds to model (model 6)
- Interaction add to model (model 6a)
- Turk add to model (model 6b)
- Therefore, all model decisions would be the same given different versions of exclusions or linking functions.
linear_e_AIC <- unlist(lapply(linear_exclude, AIC))
linear_n_AIC <- unlist(lapply(linear_no, AIC))
log_e_AIC <- unlist(lapply(log_exclude, AIC))
log_n_AIC <- unlist(lapply(log_no, AIC))
log_aics <- data.frame(
"model" = c("know1", "know2", "know3", "know4", "know5", "know6", "know6a", "know6b",
"reason1", "reason2", "reason3", "reason4", "reason5", "reason6", "reason6a", "reason6b",
"luck1", "luck2", "luck3", "luck4", "luck5", "luck6", "luck6a", "luck6b"),
"log_e_AIC" = log_e_AIC,
"log_n_AIC" = log_n_AIC
)
linear_aics <- data.frame(
"model" = c("know1", "know2", "know4", "know5", "know6", "know6a", "know6b",
"reason1", "reason2", "reason4", "reason5", "reason6", "reason6a", "reason6b",
"luck1", "luck2", "luck4", "luck5", "luck6", "luck6a", "luck6b"),
"linear_e_AIC" = linear_e_AIC,
"linear_n_AIC" = linear_n_AIC
)
aics <- log_aics %>%
full_join(linear_aics,
by = "model")
flextable(aics)model | log_e_AIC | log_n_AIC | linear_e_AIC | linear_n_AIC |
|---|---|---|---|---|
know1 | 18,881.094 | 11,990.258 | 128,678.2 | 82,273.01 |
know2 | 17,834.749 | 11,256.231 | 127,638.0 | 81,529.14 |
know3 | 17,836.749 | 11,258.231 | ||
know4 | 17,838.749 | 11,260.231 | 127,543.6 | 81,505.15 |
know5 | 17,554.309 | 11,170.385 | 125,503.8 | 80,874.04 |
know6 | 15,871.991 | 9,897.613 | 123,723.2 | 79,529.84 |
know6a | 15,807.693 | 9,846.246 | 123,506.7 | 79,347.38 |
know6b | 15,850.157 | 9,878.269 | 123,698.2 | 79,509.77 |
reason1 | 7,343.350 | 4,865.062 | 113,885.9 | 73,111.19 |
reason2 | 7,286.555 | 4,813.866 | 113,827.2 | 73,066.22 |
reason3 | 7,288.556 | 4,815.867 | ||
reason4 | 7,290.555 | 4,817.866 | 113,374.9 | 72,633.60 |
reason5 | 7,144.102 | 4,729.710 | 111,561.1 | 72,026.42 |
reason6 | 7,047.129 | 4,680.178 | 111,470.1 | 71,963.03 |
reason6a | 7,025.805 | 4,667.614 | 111,453.4 | 71,951.05 |
reason6b | 7,017.367 | 4,642.464 | 111,461.7 | 71,952.78 |
luck1 | 17,776.672 | 11,254.190 | 119,504.8 | 76,302.78 |
luck2 | 16,771.304 | 10,693.416 | 118,585.8 | 75,763.41 |
luck3 | 16,773.304 | 10,695.416 | ||
luck4 | 16,775.304 | 10,697.416 | 118,342.0 | 75,595.76 |
luck5 | 16,489.596 | 10,569.598 | 116,511.5 | 75,028.70 |
luck6 | 15,896.168 | 10,154.577 | 115,917.9 | 74,610.41 |
luck6a | 15,458.374 | 9,914.341 | 115,465.3 | 74,339.25 |
luck6b | 7,017.367 | 10,139.967 | 115,905.9 | 74,600.93 |
Knowledge
Condition
- Complete output can be seen below.
- In logistic versus linear: we find the same pattern of results for the condition variable - we cannot compare these directly because they are on different scales, however, they provide the same direction and significance decisions.
- In exclusions: both provide the same results within the same confidence intervals, directions, and significance decisions.
View full results
# log updated exclusions
summary(log_exclude[[6]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15872.0 15947.4 -7926.0 15852.0 13885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7728 -0.6220 -0.4902 0.7726 3.8696
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000 0.0000
## id:vignette (Intercept) 0.0000 0.0000
## vignette (Intercept) 0.4474 0.6689
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.183810 0.404093 -0.455 0.6492
## compYes 0.019364 0.044633 0.434 0.6644
## age 0.003733 0.001997 1.870 0.0615 .
## gender2male -0.084539 0.042604 -1.984 0.0472 *
## education -0.017069 0.007528 -2.267 0.0234 *
## condIgnorance -1.313222 0.050238 -26.140 <2e-16 ***
## condKnowledge 0.611665 0.044435 13.765 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.075
## age -0.107 0.197
## gender2male -0.030 0.014 -0.090
## education -0.235 -0.118 -0.115 0.035
## condIgnornc -0.049 0.001 -0.007 0.011 0.008
## condKnowldg -0.054 0.001 0.002 -0.006 -0.005 0.431
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# log pre-registered exclusions
summary(log_no[[6]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9897.6 9968.6 -4938.8 9877.6 8903
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0827 -0.5849 -0.4672 0.7226 4.2283
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000 0.0000
## id:vignette (Intercept) 0.0000 0.0000
## vignette (Intercept) 0.5193 0.7206
## Number of obs: 8913, groups:
## person_code:(id:vignette), 8913; id:vignette, 8913; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.288294 0.441765 -0.653 0.514
## compYes 0.076833 0.055215 1.392 0.164
## age 0.002106 0.001976 1.066 0.286
## gender2male -0.018580 0.052580 -0.353 0.724
## education -0.016070 0.010021 -1.604 0.109
## condIgnorance -1.383171 0.064406 -21.476 <2e-16 ***
## condKnowledge 0.760795 0.056327 13.507 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.058
## age -0.093 0.175
## gender2male -0.033 -0.011 -0.072
## education -0.283 -0.163 -0.114 0.014
## condIgnornc -0.055 0.000 0.003 0.004 0.003
## condKnowldg -0.062 0.003 0.004 -0.005 -0.007 0.418
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# linear updated exclusions
summary(linear_exclude[[5]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: know_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond
## Data: final_long
##
## REML criterion at convergence: 123703.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2524 -0.7884 -0.1638 0.8945 2.7775
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 34.03 5.833
## vignette (Intercept) 181.98 13.490
## Residual 1323.26 36.377
## Number of obs: 12325, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.44543 8.12911 2.33777 6.083 0.0176 *
## compYes -0.16200 1.01608 1218.64845 -0.159 0.8734
## age -0.04391 0.03910 6215.65145 -1.123 0.2615
## gender2male -0.15620 0.74241 12246.61532 -0.210 0.8334
## education -0.36543 0.13450 10237.22452 -2.717 0.0066 **
## condIgnorance -22.38826 0.80320 12225.36039 -27.874 <2e-16 ***
## condKnowledge 12.29759 0.80344 12228.00760 15.306 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.102
## age -0.106 0.167
## gender2male -0.024 -0.001 -0.026
## education -0.215 -0.036 -0.110 0.005
## condIgnornc -0.049 0.000 0.000 0.000 0.000
## condKnowldg -0.049 0.000 0.000 0.000 0.000 0.500# linear pre-registered exclusions
summary(linear_no[[5]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: know_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond
## Data: final_long
##
## REML criterion at convergence: 79509.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3121 -0.7838 -0.1363 0.8679 2.7901
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 20.86 4.567
## vignette (Intercept) 204.98 14.317
## Residual 1264.08 35.554
## Number of obs: 7961, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.32923 8.72076 2.43335 5.542 0.0197 *
## compYes 1.10405 1.16784 606.85509 0.945 0.3448
## age -0.03521 0.03315 4522.77186 -1.062 0.2883
## gender2male -0.32119 0.88876 7653.99771 -0.361 0.7178
## education -0.31052 0.17252 4395.96422 -1.800 0.0719 .
## condIgnorance -22.91682 0.97681 7912.12480 -23.461 <2e-16 ***
## condKnowledge 14.12770 0.97686 7913.61103 14.462 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.096
## age -0.085 0.146
## gender2male -0.028 -0.001 -0.002
## education -0.258 -0.056 -0.101 -0.007
## condIgnornc -0.056 0.000 0.000 0.000 0.000
## condKnowldg -0.056 0.001 0.000 0.000 0.000 0.500Vignette
- Logistic versus linear: Both show significant interactions between condition and vignette.
- Exclusions: Both show significant interactions between condition and vignette.
- The exact pattern of these interactions is different within predictors but generally has the same pattern of results.
View full results
# log updated exclusions
summary(log_exclude[[7]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15807.7 15928.3 -7887.8 15775.7 13879
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7804 -0.5717 -0.4484 0.7819 3.1287
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000172891 0.0000041580
## id:vignette (Intercept) 0.0000000000007192 0.0000008481
## vignette (Intercept) 0.0000000000000000 0.0000000000
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.555020 0.126409 4.391 0.000011300772713 ***
## compYes 0.016780 0.044844 0.374 0.708268
## age 0.003778 0.002004 1.886 0.059360 .
## gender2male -0.087674 0.042794 -2.049 0.040489 *
## education -0.016600 0.007561 -2.196 0.028122 *
## condIgnorance -1.604292 0.080023 -20.048 < 2e-16 ***
## condKnowledge 0.503172 0.076198 6.603 0.000000000040161 ***
## vignetteEmma -1.930434 0.084439 -22.862 < 2e-16 ***
## vignetteGerald -0.390320 0.073064 -5.342 0.000000091833949 ***
## condIgnorance:vignetteEmma 0.977499 0.132791 7.361 0.000000000000182 ***
## condKnowledge:vignetteEmma 0.395026 0.114528 3.449 0.000562 ***
## condIgnorance:vignetteGerald 0.223192 0.113776 1.962 0.049800 *
## condKnowledge:vignetteGerald 0.021780 0.106168 0.205 0.837455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# log pre-registered exclusions
summary(log_no[[7]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9846.2 9959.8 -4907.1 9814.2 8897
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1557 -0.5279 -0.4235 0.7544 3.2541
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00000000000000000 0.0000000000
## id:vignette (Intercept) 0.00000000000003909 0.0000001977
## vignette (Intercept) 0.00000000000000000 0.0000000000
## Number of obs: 8913, groups:
## person_code:(id:vignette), 8913; id:vignette, 8913; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.448179 0.157312 2.849 0.004386 **
## compYes 0.071208 0.055565 1.282 0.200009
## age 0.001945 0.001971 0.987 0.323752
## gender2male -0.017390 0.052887 -0.329 0.742293
## education -0.015451 0.010070 -1.534 0.124966
## condIgnorance -1.650724 0.101805 -16.215 < 2e-16 ***
## condKnowledge 0.800838 0.097344 8.227 < 2e-16 ***
## vignetteEmma -1.997144 0.108600 -18.390 < 2e-16 ***
## vignetteGerald -0.324747 0.090511 -3.588 0.000333 ***
## condIgnorance:vignetteEmma 1.048154 0.171915 6.097 0.00000000108 ***
## condKnowledge:vignetteEmma 0.218717 0.147265 1.485 0.137494
## condIgnorance:vignetteGerald 0.165680 0.144717 1.145 0.252269
## condKnowledge:vignetteGerald -0.216100 0.134482 -1.607 0.108075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# linear updated exclusions
summary(linear_exclude[[6]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: know_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * vignette
## Data: final_long
##
## REML criterion at convergence: 123474.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3300 -0.6937 -0.2529 0.8658 2.5602
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 34.9 5.907
## vignette (Intercept) 112.3 10.596
## Residual 1302.1 36.084
## Number of obs: 12325, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 63.6491120328 10.9056667857 0.0000003184
## compYes -0.1585251266 1.0118597217 1262.5701557380
## age -0.0471365522 0.0388419350 6404.4544013999
## gender2male -0.3060476336 0.7367621855 12251.4458417057
## education -0.3309000164 0.1335614390 10325.0479322773
## condIgnorance -33.0611724830 1.3871976459 12222.8735524039
## condKnowledge 9.6693642569 1.3734366746 12223.2342014353
## vignetteEmma -36.1750949383 15.1201244986 0.0000002942
## vignetteGerald -7.6123993344 15.1198092726 0.0000002941
## condIgnorance:vignetteEmma 25.5616991268 1.9565836922 12225.4808225861
## condKnowledge:vignetteEmma 6.3093059824 1.9483982067 12227.4572082872
## condIgnorance:vignetteGerald 6.3550643077 1.9527921309 12219.9613719969
## condKnowledge:vignetteGerald 1.5564721118 1.9516682843 12220.5235542453
## t value Pr(>|t|)
## (Intercept) 5.836 1.00000
## compYes -0.157 0.87553
## age -1.214 0.22497
## gender2male -0.415 0.67786
## education -2.478 0.01325 *
## condIgnorance -23.833 < 2e-16 ***
## condKnowledge 7.040 0.00000000000202 ***
## vignetteEmma -2.393 1.00000
## vignetteGerald -0.503 1.00000
## condIgnorance:vignetteEmma 13.064 < 2e-16 ***
## condKnowledge:vignetteEmma 3.238 0.00121 **
## condIgnorance:vignetteGerald 3.254 0.00114 **
## condKnowledge:vignetteGerald 0.798 0.42517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# linear pre-registered exclusions
summary(linear_no[[6]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: know_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * vignette
## Data: final_long
##
## REML criterion at convergence: 79315.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4492 -0.6723 -0.2367 0.8142 2.5545
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 22.42 4.734
## vignette (Intercept) 168.55 12.983
## Residual 1238.46 35.192
## Number of obs: 7961, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 62.62863 13.34194 7831.79354 4.694
## compYes 0.86673 1.16506 657.59378 0.744
## age -0.04477 0.03290 4792.01211 -1.361
## gender2male -0.52381 0.88077 7697.21219 -0.595
## education -0.24715 0.17125 4577.13556 -1.443
## condIgnorance -33.31477 1.68387 7907.13101 -19.785
## condKnowledge 14.22666 1.66097 7908.92749 8.565
## vignetteEmma -37.32864 18.51654 7905.01302 -2.016
## vignetteGerald -7.01338 18.51535 7905.70317 -0.379
## condIgnorance:vignetteEmma 26.39881 2.37794 7912.72612 11.102
## condKnowledge:vignetteEmma 2.99716 2.36189 7913.98767 1.269
## condIgnorance:vignetteGerald 4.68784 2.36824 7906.36535 1.979
## condKnowledge:vignetteGerald -3.20824 2.36675 7906.17704 -1.356
## Pr(>|t|)
## (Intercept) 0.00000272 ***
## compYes 0.4572
## age 0.1736
## gender2male 0.5520
## education 0.1490
## condIgnorance < 2e-16 ***
## condKnowledge < 2e-16 ***
## vignetteEmma 0.0438 *
## vignetteGerald 0.7049
## condIgnorance:vignetteEmma < 2e-16 ***
## condKnowledge:vignetteEmma 0.2045
## condIgnorance:vignetteGerald 0.0478 *
## condKnowledge:vignetteGerald 0.1753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluesTurk
- Logistic versus linear: In this case, logistic models do not show an interaction between the Turk sample and the condition. Linear models show an interaction.
- Exclusions: exclusions do not appear to change the pattern of results within each linking function.
- The effect for linear models is graphed below. This result occurs because there is slightly more sensitivity to show the differences between Gettier and Knowledge in Turk than the other sample. A visualization of the distributions shows that more people pick zero in the regular sample than Turk.
View full result
# log updated exclusions
summary(log_exclude[[8]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15850.2 15948.2 -7912.1 15824.2 13882
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1117 -0.6244 -0.4852 0.7822 3.9470
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000002108 0.0000004591
## id:vignette (Intercept) 0.0000000000024070 0.0000015515
## vignette (Intercept) 0.4494139896495767 0.6703834646
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0743861 0.4058531 -0.183 0.85458
## compYes -0.0330383 0.0463320 -0.713 0.47580
## age -0.0001138 0.0022169 -0.051 0.95906
## gender2male -0.1113136 0.0431663 -2.579 0.00992 **
## education -0.0164575 0.0075305 -2.185 0.02886 *
## condIgnorance -1.2894721 0.0521660 -24.719 < 2e-16 ***
## condKnowledge 0.5914056 0.0461318 12.820 < 2e-16 ***
## turkTRUE 0.3226120 0.1260957 2.558 0.01051 *
## condIgnorance:turkTRUE -0.3324922 0.1930846 -1.722 0.08507 .
## condKnowledge:turkTRUE 0.2986885 0.1735316 1.721 0.08521 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.090
## age -0.124 0.288
## gender2male -0.039 0.053 -0.021
## education -0.233 -0.119 -0.112 0.032
## condIgnornc -0.052 0.006 0.001 0.011 0.007
## condKnowldg -0.055 -0.001 -0.002 -0.006 -0.005 0.433
## turkTRUE 0.031 -0.182 -0.282 -0.098 0.011 0.158 0.184
## cndIgn:TRUE 0.013 -0.001 0.001 0.007 0.001 -0.268 -0.119 -0.588
## cndKnw:TRUE 0.014 0.000 0.002 -0.001 0.002 -0.118 -0.263 -0.654 0.427
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# log pre-registered exclusions
summary(log_no[[8]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9878.3 9970.5 -4926.1 9852.3 8900
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1719 -0.5885 -0.4616 0.7383 4.3004
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.000000000000000 0.000000000
## id:vignette (Intercept) 0.000000000002026 0.000001423
## vignette (Intercept) 0.523661794694040 0.723644799
## Number of obs: 8913, groups:
## person_code:(id:vignette), 8913; id:vignette, 8913; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.161654 0.444460 -0.364 0.71608
## compYes -0.005201 0.058036 -0.090 0.92859
## age -0.001795 0.002162 -0.830 0.40649
## gender2male -0.059996 0.053492 -1.122 0.26204
## education -0.016217 0.010029 -1.617 0.10587
## condIgnorance -1.353173 0.068607 -19.723 < 2e-16 ***
## condKnowledge 0.744792 0.059787 12.457 < 2e-16 ***
## turkTRUE 0.408205 0.130505 3.128 0.00176 **
## condIgnorance:turkTRUE -0.280348 0.198922 -1.409 0.15874
## condKnowledge:turkTRUE 0.160700 0.178869 0.898 0.36896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.074
## age -0.110 0.276
## gender2male -0.043 0.042 0.001
## education -0.282 -0.153 -0.103 0.015
## condIgnornc -0.060 0.009 0.013 0.005 0.002
## condKnowldg -0.064 -0.001 -0.003 -0.006 -0.008 0.421
## turkTRUE 0.025 -0.209 -0.270 -0.116 -0.005 0.190 0.229
## cndIgn:TRUE 0.019 -0.002 0.000 0.007 0.002 -0.342 -0.149 -0.586
## cndKnw:TRUE 0.021 0.000 0.001 0.001 0.003 -0.144 -0.329 -0.652 0.427
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# linear updated exclusions
summary(linear_exclude[[7]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: know_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * turk
## Data: final_long
##
## REML criterion at convergence: 123672.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2367 -0.7893 -0.1609 0.8931 2.7698
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 32.43 5.695
## vignette (Intercept) 182.13 13.496
## Residual 1321.89 36.358
## Number of obs: 12325, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 49.52458 8.13216 2.33994 6.090 0.01755 *
## compYes -0.41102 1.01706 1255.78125 -0.404 0.68619
## age -0.05609 0.03961 8939.44523 -1.416 0.15680
## gender2male -0.23396 0.74291 12292.84701 -0.315 0.75283
## education -0.36273 0.13433 10055.14842 -2.700 0.00694 **
## condIgnorance -21.71751 0.83998 12224.24000 -25.855 < 2e-16 ***
## condKnowledge 12.03232 0.84033 12227.71322 14.319 < 2e-16 ***
## turkTRUE 8.62820 3.96644 86.66971 2.175 0.03233 *
## condIgnorance:turkTRUE -7.76061 2.85394 12210.81748 -2.719 0.00655 **
## condKnowledge:turkTRUE 3.05294 2.85177 12205.16782 1.071 0.28440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.103
## age -0.106 0.184
## gender2male -0.025 0.005 -0.017
## education -0.214 -0.037 -0.110 0.005
## condIgnornc -0.052 0.000 0.000 0.000 0.000
## condKnowldg -0.052 0.000 0.000 0.000 0.000 0.500
## turkTRUE 0.005 -0.099 -0.157 -0.048 0.008 0.106 0.106
## cndIgn:TRUE 0.015 0.000 0.000 0.000 0.000 -0.294 -0.147 -0.360
## cndKnw:TRUE 0.015 0.000 0.000 0.000 0.000 -0.147 -0.295 -0.359 0.500# linear pre-registered exclusions
summary(linear_no[[7]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: know_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * turk
## Data: final_long
##
## REML criterion at convergence: 79483.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2831 -0.7844 -0.1323 0.8648 2.7717
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 18.48 4.299
## vignette (Intercept) 205.62 14.339
## Residual 1262.84 35.536
## Number of obs: 7961, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.21268 8.72957 2.43048 5.523 0.01991 *
## compYes 0.76590 1.16822 608.31613 0.656 0.51232
## age -0.04488 0.03357 6812.15964 -1.337 0.18128
## gender2male -0.44964 0.89100 7881.38786 -0.505 0.61382
## education -0.31490 0.17195 3941.88302 -1.831 0.06713 .
## condIgnorance -21.90260 1.04918 7910.63850 -20.876 < 2e-16 ***
## condKnowledge 13.97736 1.04938 7913.05007 13.320 < 2e-16 ***
## turkTRUE 7.77227 3.33795 43.43117 2.328 0.02461 *
## condIgnorance:turkTRUE -7.57655 2.86480 7901.25946 -2.645 0.00819 **
## condKnowledge:turkTRUE 1.10771 2.86284 7895.37692 0.387 0.69882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.095
## age -0.084 0.170
## gender2male -0.028 0.012 0.012
## education -0.257 -0.057 -0.100 -0.007
## condIgnornc -0.060 0.001 0.000 0.000 0.000
## condKnowldg -0.060 0.001 0.000 0.000 0.000 0.500
## turkTRUE -0.005 -0.128 -0.152 -0.076 -0.001 0.157 0.157
## cndIgn:TRUE 0.022 0.000 0.000 0.000 0.000 -0.366 -0.183 -0.429
## cndKnw:TRUE 0.022 0.000 0.000 0.000 0.000 -0.183 -0.367 -0.429 0.500df <- linear_exclude[[7]]@frame
ggplot(df, aes(turk, know_vas, fill = cond)) +
stat_summary(fun = mean,
geom = "bar",
position = "dodge") +
stat_summary(fun.data = mean_cl_normal,
geom = "errorbar",
position = position_dodge(width = 0.90),
width = .2) +
theme_classic() +
xlab("Turk Sample") +
ylab("Knowledge Rating") +
scale_fill_discrete(name = "Condition")
ggplot(df, aes(turk, know_vas)) +
geom_violin() +
theme_classic() +
xlab("Turk Sample") +
ylab("Knowledge Rating")
Reasonable
Condition
- Logistic versus linear: The pattern, direction, and magnitude of results is the same.
- Exclusions: The pattern, direction, and magnitude of results is the same.
View full results
# log updated exclusions
summary(log_exclude[[14]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7047.1 7122.6 -3513.6 7027.1 13964
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6491 0.2093 0.2544 0.3073 0.5823
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.001461 0.03822
## id:vignette (Intercept) 0.007296 0.08542
## vignette (Intercept) 0.076656 0.27687
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.839819 0.248928 7.391 0.000000000000146 ***
## compYes 0.251565 0.072504 3.470 0.000521 ***
## age -0.004968 0.003209 -1.548 0.121581
## gender2male -0.181153 0.070632 -2.565 0.010326 *
## education 0.057713 0.011926 4.839 0.000001303215506 ***
## condIgnorance -0.400271 0.075940 -5.271 0.000000135772713 ***
## condKnowledge 0.425613 0.090671 4.694 0.000002678579960 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.202
## age -0.297 0.201
## gender2male -0.085 0.014 -0.095
## education -0.599 -0.098 -0.097 0.030
## condIgnornc -0.178 -0.005 0.002 0.002 0.001
## condKnowldg -0.154 0.003 0.003 -0.002 0.007 0.487# log pre-registered exclusions
summary(log_no[[14]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 4680.2 4751.2 -2330.1 4660.2 8963
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.0840 0.2131 0.2619 0.3188 0.7567
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00065586043 0.0256098
## id:vignette (Intercept) 0.00000001395 0.0001181
## vignette (Intercept) 0.11065854035 0.3326538
## Number of obs: 8973, groups:
## person_code:(id:vignette), 8973; id:vignette, 8973; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.554670 0.291704 5.330 0.0000000984 ***
## compYes 0.121551 0.087029 1.397 0.162510
## age -0.006759 0.002591 -2.608 0.009097 **
## gender2male -0.153204 0.084696 -1.809 0.070471 .
## education 0.084209 0.014909 5.648 0.0000000162 ***
## condIgnorance -0.308554 0.092775 -3.326 0.000882 ***
## condKnowledge 0.429622 0.108894 3.945 0.0000796927 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.152
## age -0.184 0.143
## gender2male -0.089 -0.004 -0.056
## education -0.627 -0.119 -0.115 0.002
## condIgnornc -0.180 -0.005 0.000 0.002 0.002
## condKnowldg -0.158 0.002 -0.001 -0.005 0.009 0.482# linear updated exclusions
summary(linear_exclude[[12]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: reason_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond
## Data: final_long
##
## REML criterion at convergence: 111450.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2604 -0.2205 0.3649 0.5607 2.1477
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 33.403 5.780
## vignette (Intercept) 4.498 2.121
## Residual 484.285 22.006
## Number of obs: 12331, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 82.54112 1.93942 8.13361 42.560 0.00000000007562 ***
## compYes 0.73805 0.66528 3656.02132 1.109 0.2673
## age 0.01920 0.02426 10691.79619 0.791 0.4288
## gender2male -1.15252 0.45093 12317.81232 -2.556 0.0106 *
## education 0.17174 0.08245 12002.02033 2.083 0.0373 *
## condIgnorance -3.36510 0.48594 12226.71495 -6.925 0.00000000000458 ***
## condKnowledge 1.19634 0.48600 12228.01231 2.462 0.0138 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.277
## age -0.272 0.145
## gender2male -0.062 -0.007 -0.019
## education -0.553 -0.028 -0.111 0.004
## condIgnornc -0.125 0.000 0.000 0.000 0.000
## condKnowldg -0.125 0.000 0.000 0.000 0.000 0.500# linear pre-registered exclusions
summary(linear_no[[12]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: reason_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond
## Data: final_long
##
## REML criterion at convergence: 71943
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2647 -0.2465 0.3656 0.5597 2.2324
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 55.366 7.441
## vignette (Intercept) 5.225 2.286
## Residual 481.008 21.932
## Number of obs: 7966, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 81.25953 2.41740 8.30856 33.614 0.000000000358 ***
## compYes 0.69844 0.81085 4583.74670 0.861 0.3891
## age -0.02399 0.02102 7863.88774 -1.141 0.2537
## gender2male -1.13277 0.55242 7950.42605 -2.051 0.0403 *
## education 0.23547 0.10979 7776.50097 2.145 0.0320 *
## condIgnorance -3.26289 0.60258 7909.17481 -5.415 0.000000063118 ***
## condKnowledge 1.53964 0.60271 7909.70465 2.555 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.246
## age -0.197 0.114
## gender2male -0.064 -0.010 0.013
## education -0.603 -0.033 -0.089 -0.005
## condIgnornc -0.125 0.000 0.000 0.000 0.000
## condKnowldg -0.125 0.000 0.000 0.000 0.000 0.500Vignette
- Logistic versus linear: Generally, each version showed an interaction of condition and vignette.
- Exclusions: Logistic models both showed interactions for each exclusion type. However, the linear model with the pre-registered exclusions did not show an interaction between condition and vignette. This model has significantly less power than the updated exclusions, as only has 8000 data points (~2600+ participants) versus 12000 + data points (~4100+ participants).
View full results
# log updated exclusions
summary(log_exclude[[15]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7025.8 7146.5 -3496.9 6993.8 13958
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4722 0.2020 0.2562 0.3173 0.5561
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.000000 0.00000
## id:vignette (Intercept) 0.002124 0.04609
## vignette (Intercept) 0.000000 0.00000
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.425110 0.222940 10.878 < 2e-16 ***
## compYes 0.248903 0.072555 3.431 0.000602 ***
## age -0.005006 0.003218 -1.556 0.119764
## gender2male -0.181886 0.070666 -2.574 0.010056 *
## education 0.058594 0.011936 4.909 0.0000009160223 ***
## condIgnorance -0.863705 0.160582 -5.379 0.0000000750731 ***
## condKnowledge 0.390875 0.204642 1.910 0.056127 .
## vignetteEmma -1.099565 0.155615 -7.066 0.0000000000016 ***
## vignetteGerald -0.522306 0.168276 -3.104 0.001910 **
## condIgnorance:vignetteEmma 0.741195 0.196886 3.765 0.000167 ***
## condKnowledge:vignetteEmma 0.256617 0.245839 1.044 0.296559
## condIgnorance:vignetteGerald 0.424379 0.210062 2.020 0.043357 *
## condKnowledge:vignetteGerald -0.237181 0.255828 -0.927 0.353869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# log pre-registered exclusions
summary(log_no[[15]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 4667.6 4781.2 -2317.8 4635.6 8957
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.7634 0.2141 0.2683 0.3201 0.7372
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 4.000e-14 2.000e-07
## id:vignette (Intercept) 4.000e-14 2.000e-07
## vignette (Intercept) 3.388e-47 5.821e-24
## Number of obs: 8973, groups:
## person_code:(id:vignette), 8973; id:vignette, 8973; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.059901 0.255214 8.071 6.96e-16 ***
## compYes 0.117747 0.087086 1.352 0.17635
## age -0.006854 0.002595 -2.641 0.00826 **
## gender2male -0.149654 0.084732 -1.766 0.07736 .
## education 0.084600 0.014902 5.677 1.37e-08 ***
## condIgnorance -0.569533 0.193958 -2.936 0.00332 **
## condKnowledge 0.770953 0.263458 2.926 0.00343 **
## vignetteEmma -0.995789 0.182261 -5.464 4.67e-08 ***
## vignetteGerald -0.450185 0.196305 -2.293 0.02183 *
## condIgnorance:vignetteEmma 0.456683 0.238897 1.912 0.05592 .
## condKnowledge:vignetteEmma -0.215650 0.309199 -0.697 0.48552
## condIgnorance:vignetteGerald 0.180623 0.253647 0.712 0.47640
## condKnowledge:vignetteGerald -0.671010 0.319988 -2.097 0.03600 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# linear updated exclusions
summary(linear_exclude[[13]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: reason_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * vignette
## Data: final_long
##
## REML criterion at convergence: 111421.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2969 -0.2239 0.3612 0.5692 2.1092
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 33.469 5.785
## vignette (Intercept) 9.252 3.042
## Residual 484.005 22.000
## Number of obs: 12331, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 84.16532 3.51892 6505.36624 23.918
## compYes 0.73661 0.66519 3664.90648 1.107
## age 0.01918 0.02426 10694.81320 0.791
## gender2male -1.16314 0.45090 12313.79453 -2.580
## education 0.17602 0.08246 11996.71032 2.135
## condIgnorance -4.84758 0.84596 12224.28429 -5.730
## condKnowledge 1.68797 0.83740 12224.08360 2.016
## vignetteEmma -5.03101 4.60179 5888.85314 -1.093
## vignetteGerald -0.01318 4.60122 5889.51260 -0.003
## condIgnorance:vignetteEmma 3.04640 1.19323 12225.73295 2.553
## condKnowledge:vignetteEmma -0.21466 1.18802 12226.32527 -0.181
## condIgnorance:vignetteGerald 1.37575 1.19056 12222.90307 1.156
## condKnowledge:vignetteGerald -1.28468 1.18963 12223.18341 -1.080
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## compYes 0.2682
## age 0.4292
## gender2male 0.0099 **
## education 0.0328 *
## condIgnorance 0.0000000103 ***
## condKnowledge 0.0439 *
## vignetteEmma 0.2743
## vignetteGerald 0.9977
## condIgnorance:vignetteEmma 0.0107 *
## condKnowledge:vignetteEmma 0.8566
## condIgnorance:vignetteGerald 0.2479
## condKnowledge:vignetteGerald 0.2802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?# linear pre-registered exclusions
summary(linear_no[[13]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: reason_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * vignette
## Data: final_long
##
## REML criterion at convergence: 71919.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2507 -0.2474 0.3620 0.5689 2.2114
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 55.43 7.445
## vignette (Intercept) 11.75 3.428
## Residual 481.03 21.932
## Number of obs: 7966, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 83.19236731081 4.27752426252 0.00000006172
## compYes 0.68268359425 0.81098410528 4587.17902672185
## age -0.02408865268 0.02103202742 7859.68291505886
## gender2male -1.13072158873 0.55279163395 7946.56926150827
## education 0.23739661911 0.10989376237 7770.25000475278
## condIgnorance -3.55277098209 1.04985498625 7905.58201463511
## condKnowledge 2.50288080765 1.03530481729 7905.98212640020
## vignetteEmma -5.62445886327 5.56692185351 0.00000004426
## vignetteGerald -0.23120400095 5.56469801642 0.00000004419
## condIgnorance:vignetteEmma 1.19673534751 1.48284561261 7907.31634547197
## condKnowledge:vignetteEmma -0.86268044461 1.47265965270 7907.62722968159
## condIgnorance:vignetteGerald -0.33666307818 1.47566823976 7905.12887209355
## condKnowledge:vignetteGerald -2.04740546590 1.47497461086 7905.18687022792
## t value Pr(>|t|)
## (Intercept) 19.449 0.999999
## compYes 0.842 0.399946
## age -1.145 0.252107
## gender2male -2.045 0.040841 *
## education 2.160 0.030785 *
## condIgnorance -3.384 0.000718 ***
## condKnowledge 2.418 0.015649 *
## vignetteEmma -1.010 1.000000
## vignetteGerald -0.042 1.000000
## condIgnorance:vignetteEmma 0.807 0.419660
## condKnowledge:vignetteEmma -0.586 0.558028
## condIgnorance:vignetteGerald -0.228 0.819541
## condKnowledge:vignetteGerald -1.388 0.165147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Turk
- Logistic versus linear: Generally, this shows the same pattern, direction, and magnitude of results.
- Exclusions: Given the dichotomization of p-values, the linear model with pre-registered exclusions does show one significant interaction between Turk and condition. However, the estimates each overlap within confidence interval, magnitude, and direction.
View full results
# log updated exclusions
summary(log_exclude[[16]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7017.4 7115.5 -3495.7 6991.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5047 0.2054 0.2546 0.3087 0.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0023770476 0.0487550
## id:vignette (Intercept) 0.0000001472 0.0003837
## vignette (Intercept) 0.0768855931 0.2772825
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.073866 0.251554 8.244 < 2e-16 ***
## compYes 0.131776 0.075708 1.741 0.081757 .
## age -0.012466 0.003376 -3.692 0.000222 ***
## gender2male -0.232380 0.071089 -3.269 0.001080 **
## education 0.056843 0.011725 4.848 0.00000125 ***
## condIgnorance -0.378343 0.077451 -4.885 0.00000103 ***
## condKnowledge 0.440103 0.092314 4.767 0.00000187 ***
## turkTRUE 1.385038 0.368873 3.755 0.000173 ***
## condIgnorance:turkTRUE -0.655384 0.427927 -1.532 0.125638
## condKnowledge:turkTRUE -0.443704 0.514800 -0.862 0.388745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.251
## age -0.338 0.315
## gender2male -0.109 0.053 -0.039
## education -0.583 -0.094 -0.088 0.035
## condIgnornc -0.180 -0.003 0.005 0.004 0.001
## condKnowldg -0.154 0.002 0.001 -0.003 0.007 0.484
## turkTRUE 0.044 -0.113 -0.156 -0.050 -0.003 0.120 0.101
## cndIgn:TRUE 0.032 0.001 0.001 -0.002 -0.001 -0.181 -0.088 -0.835
## cndKnw:TRUE 0.027 0.000 0.001 0.001 -0.002 -0.087 -0.179 -0.694 0.598# log pre-registered exclusions
summary(log_no[[16]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 4642.5 4734.8 -2308.2 4616.5 8960
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.0531 0.2029 0.2619 0.3207 1.3985
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00000000001987 0.000004457
## id:vignette (Intercept) 0.00727367508328 0.085285843
## vignette (Intercept) 0.11192331507671 0.334549421
## Number of obs: 8973, groups:
## person_code:(id:vignette), 8973; id:vignette, 8973; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.757931 0.291438 6.032 0.00000000162 ***
## compYes -0.035211 0.089757 -0.392 0.69484
## age -0.011293 0.002441 -4.627 0.00000371868 ***
## gender2male -0.238548 0.085714 -2.783 0.00538 **
## education 0.079720 0.014614 5.455 0.00000004900 ***
## condIgnorance -0.268770 0.095790 -2.806 0.00502 **
## condKnowledge 0.454173 0.111954 4.057 0.00004975103 ***
## turkTRUE 1.515018 0.369668 4.098 0.00004161549 ***
## condIgnorance:turkTRUE -0.760031 0.431834 -1.760 0.07841 .
## condKnowledge:turkTRUE -0.459598 0.518873 -0.886 0.37575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.182
## age -0.190 0.216
## gender2male -0.114 0.049 -0.003
## education -0.621 -0.104 -0.103 0.010
## condIgnornc -0.185 -0.003 0.003 0.005 0.003
## condKnowldg -0.160 -0.002 -0.008 -0.008 0.010 0.476
## turkTRUE 0.014 -0.105 -0.108 -0.063 -0.016 0.144 0.124
## cndIgn:TRUE 0.042 0.001 0.000 -0.003 -0.001 -0.222 -0.105 -0.836
## cndKnw:TRUE 0.034 0.001 0.003 0.003 -0.003 -0.103 -0.216 -0.696 0.596
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# linear updated exclusions
summary(linear_exclude[[14]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: reason_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * turk
## Data: final_long
##
## REML criterion at convergence: 111435.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2586 -0.2228 0.3620 0.5617 2.1431
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 33.489 5.787
## vignette (Intercept) 4.497 2.121
## Residual 484.215 22.005
## Number of obs: 12331, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 82.37440 1.94146 8.16506 42.429
## compYes 0.69705 0.66691 3828.65021 1.045
## age 0.01684 0.02441 11602.27835 0.690
## gender2male -1.16566 0.45115 12306.98980 -2.584
## education 0.17214 0.08244 11995.51729 2.088
## condIgnorance -3.07832 0.50842 12225.41625 -6.055
## condKnowledge 1.36964 0.50854 12226.99384 2.693
## turkTRUE 4.89406 3.63052 97.44898 1.348
## condIgnorance:turkTRUE -3.30979 1.72740 12217.78311 -1.916
## condKnowledge:turkTRUE -1.99600 1.72596 12216.04908 -1.156
## Pr(>|t|)
## (Intercept) 0.0000000000722 ***
## compYes 0.29600
## age 0.49030
## gender2male 0.00978 **
## education 0.03683 *
## condIgnorance 0.0000000014486 ***
## condKnowledge 0.00708 **
## turkTRUE 0.18077
## condIgnorance:turkTRUE 0.05538 .
## condKnowledge:turkTRUE 0.24752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.275
## age -0.269 0.151
## gender2male -0.061 -0.005 -0.015
## education -0.552 -0.028 -0.111 0.003
## condIgnornc -0.131 0.000 0.000 0.000 0.000
## condKnowldg -0.131 0.000 0.000 0.000 0.000 0.500
## turkTRUE -0.020 -0.066 -0.104 -0.031 0.006 0.070 0.070
## cndIgn:TRUE 0.039 0.000 0.000 0.000 0.000 -0.294 -0.147 -0.238
## cndKnw:TRUE 0.039 0.000 0.000 0.000 0.000 -0.147 -0.295 -0.238 0.500# linear pre-registered exclusions
summary(linear_no[[14]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: reason_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * turk
## Data: final_long
##
## REML criterion at convergence: 71926.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2677 -0.2457 0.3587 0.5630 2.2243
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 54.909 7.410
## vignette (Intercept) 5.261 2.294
## Residual 480.864 21.929
## Number of obs: 7966, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 80.76648 2.42892 8.44710 33.252 0.000000000297
## compYes 0.63938 0.81200 4721.98086 0.787 0.43107
## age -0.02568 0.02107 7935.30911 -1.219 0.22282
## gender2male -1.15483 0.55265 7939.35699 -2.090 0.03668
## education 0.23552 0.10977 7761.41301 2.146 0.03194
## condIgnorance -2.77856 0.64746 7907.64823 -4.291 0.000017961265
## condKnowledge 1.87603 0.64772 7908.35149 2.896 0.00379
## turkTRUE 7.28019 4.59949 48.16052 1.583 0.12001
## condIgnorance:turkTRUE -3.61045 1.76800 7904.28369 -2.042 0.04117
## condKnowledge:turkTRUE -2.50244 1.76665 7903.69215 -1.416 0.15667
##
## (Intercept) ***
## compYes
## age
## gender2male *
## education *
## condIgnorance ***
## condKnowledge **
## turkTRUE
## condIgnorance:turkTRUE *
## condKnowledge:turkTRUE
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.240
## age -0.190 0.117
## gender2male -0.061 -0.008 0.015
## education -0.600 -0.033 -0.089 -0.005
## condIgnornc -0.133 0.000 0.000 0.000 0.000
## condKnowldg -0.133 0.000 0.000 0.000 0.000 0.501
## turkTRUE -0.088 -0.059 -0.067 -0.033 -0.001 0.070 0.070
## cndIgn:TRUE 0.049 0.000 0.000 0.000 0.000 -0.366 -0.183 -0.192
## cndKnw:TRUE 0.049 0.000 0.000 0.000 0.000 -0.184 -0.367 -0.192 0.500Luck
Condition
- Logistic versus linear: Models show the same results.
- Exclusions: Models show the same results.
View full results
# log updated exclusions
summary(log_exclude[[22]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15896.2 15970.6 -7938.1 15876.2 12630
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1575 -0.8039 -0.4574 0.9076 2.2438
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.009841 0.09920
## id:vignette (Intercept) 0.004391 0.06626
## vignette (Intercept) 0.373469 0.61112
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.246316 0.372248 -0.662 0.5082
## compYes -0.180694 0.043976 -4.109 0.0000398 ***
## age 0.004153 0.001967 2.111 0.0348 *
## gender2male 0.007267 0.042185 0.172 0.8632
## education -0.031096 0.007485 -4.155 0.0000326 ***
## condIgnorance 1.027734 0.047414 21.676 < 2e-16 ***
## condKnowledge 0.935679 0.047106 19.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.079
## age -0.116 0.191
## gender2male -0.029 0.014 -0.086
## education -0.253 -0.116 -0.109 0.023
## condIgnornc -0.064 -0.007 0.018 0.002 -0.014
## condKnowldg -0.062 -0.012 0.009 -0.001 -0.017 0.525# log pre-registered exclusions
summary(log_no[[22]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 10154.6 10224.5 -5067.3 10134.6 8044
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1437 -0.8239 -0.4450 0.9135 2.3238
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.007540 0.08683
## id:vignette (Intercept) 0.005717 0.07561
## vignette (Intercept) 0.336196 0.57982
## Number of obs: 8054, groups:
## person_code:(id:vignette), 8054; id:vignette, 8054; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.273730 0.365110 -0.750 0.45342
## compYes -0.280924 0.053476 -5.253 0.000000149 ***
## age 0.003280 0.001852 1.771 0.07656 .
## gender2male 0.072560 0.051339 1.413 0.15755
## education -0.028566 0.009833 -2.905 0.00367 **
## condIgnorance 1.036935 0.059455 17.441 < 2e-16 ***
## condKnowledge 1.030595 0.059157 17.421 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.062
## age -0.109 0.165
## gender2male -0.035 -0.011 -0.065
## education -0.336 -0.163 -0.104 -0.004
## condIgnornc -0.082 -0.018 0.018 0.014 -0.012
## condKnowldg -0.079 -0.022 0.014 0.004 -0.019 0.530# linear updated exclusions
summary(linear_exclude[[19]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: luck_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond
## Data: final_luck
##
## REML criterion at convergence: 115897.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20047 -0.82864 -0.05521 0.88539 2.41224
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 51.54 7.179
## vignette (Intercept) 174.48 13.209
## Residual 1385.09 37.217
## Number of obs: 11492, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.54639 8.03158 2.40667 5.048 0.02497 *
## compYes -2.72419 1.11071 1652.21090 -2.453 0.01428 *
## age 0.11542 0.04174 7540.19056 2.765 0.00571 **
## gender2male -0.53176 0.78840 11471.61637 -0.674 0.50002
## education -0.24143 0.14382 10385.21417 -1.679 0.09324 .
## condIgnorance 17.92923 0.85423 11400.80415 20.989 < 2e-16 ***
## condKnowledge 18.57941 0.85105 11402.71373 21.831 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.113
## age -0.114 0.158
## gender2male -0.025 -0.006 -0.020
## education -0.233 -0.030 -0.111 0.002
## condIgnornc -0.054 0.002 0.005 0.001 0.001
## condKnowldg -0.054 0.004 0.001 -0.001 -0.001 0.503# linear pre-registered exclusions
summary(linear_no[[19]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: luck_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond
## Data: final_luck
##
## REML criterion at convergence: 74590.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.14158 -0.82815 -0.06471 0.88672 2.44212
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 48.59 6.971
## vignette (Intercept) 151.21 12.297
## Residual 1349.78 36.739
## Number of obs: 7417, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 38.04904 7.75370 2.71634 4.907 0.0204 *
## compYes -2.89810 1.32303 1321.58037 -2.190 0.0287 *
## age 0.08402 0.03532 6267.16102 2.379 0.0174 *
## gender2male -0.07517 0.95661 7372.66695 -0.079 0.9374
## education -0.10216 0.18776 5977.08222 -0.544 0.5864
## condIgnorance 17.51188 1.05058 7369.73658 16.669 <2e-16 ***
## condKnowledge 19.95222 1.04543 7372.04850 19.085 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.124
## age -0.102 0.129
## gender2male -0.032 -0.010 0.006
## education -0.319 -0.042 -0.095 -0.010
## condIgnornc -0.069 0.002 0.001 0.002 0.003
## condKnowldg -0.069 0.003 0.000 0.000 0.000 0.504Vignette
- Logistic versus linear: Models show the same pattern of interaction results.
- Exclusions: Models show the same pattern of interaction results.
View full results
# log updated exclusions
summary(log_exclude[[23]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15458.4 15577.5 -7713.2 15426.4 12624
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1894 -1.0118 -0.2784 0.8435 3.7019
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0004629 0.02151
## id:vignette (Intercept) 0.0015660 0.03957
## vignette (Intercept) 0.0000000 0.00000
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.907304 0.128577 7.056 0.0000000000017077
## compYes -0.195308 0.044696 -4.370 0.0000124396016865
## age 0.004152 0.002002 2.074 0.0381
## gender2male -0.004211 0.042725 -0.099 0.9215
## education -0.031082 0.007607 -4.086 0.0000439445273061
## condIgnorance 0.083929 0.078418 1.070 0.2845
## condKnowledge 0.723553 0.082462 8.774 < 2e-16
## vignetteEmma -2.851015 0.110619 -25.773 < 2e-16
## vignetteGerald -1.070444 0.078819 -13.581 < 2e-16
## condIgnorance:vignetteEmma 2.527798 0.135060 18.716 < 2e-16
## condKnowledge:vignetteEmma 1.067342 0.138234 7.721 0.0000000000000115
## condIgnorance:vignetteGerald 0.723264 0.110503 6.545 0.0000000000594091
## condKnowledge:vignetteGerald 0.094904 0.113794 0.834 0.4043
##
## (Intercept) ***
## compYes ***
## age *
## gender2male
## education ***
## condIgnorance
## condKnowledge ***
## vignetteEmma ***
## vignetteGerald ***
## condIgnorance:vignetteEmma ***
## condKnowledge:vignetteEmma ***
## condIgnorance:vignetteGerald ***
## condKnowledge:vignetteGerald
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# log pre-registered exclusions
summary(log_no[[23]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9914.3 10026.2 -4941.2 9882.3 8038
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3215 -0.9978 -0.3065 0.8821 3.3888
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00000006354 0.0002521
## id:vignette (Intercept) 0.00062112104 0.0249223
## vignette (Intercept) 0.00000000000 0.0000000
## Number of obs: 8054, groups:
## person_code:(id:vignette), 8054; id:vignette, 8054; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.726340 0.157160 4.622 0.0000038069 ***
## compYes -0.310590 0.054287 -5.721 0.0000000106 ***
## age 0.002672 0.001875 1.425 0.15405
## gender2male 0.071172 0.051920 1.371 0.17044
## education -0.027225 0.009964 -2.732 0.00629 **
## condIgnorance 0.216393 0.097719 2.214 0.02680 *
## condKnowledge 1.041799 0.103708 10.045 < 2e-16 ***
## vignetteEmma -2.420701 0.129448 -18.700 < 2e-16 ***
## vignetteGerald -0.881518 0.098234 -8.974 < 2e-16 ***
## condIgnorance:vignetteEmma 2.144465 0.161993 13.238 < 2e-16 ***
## condKnowledge:vignetteEmma 0.506074 0.166742 3.035 0.00240 **
## condIgnorance:vignetteGerald 0.592601 0.138050 4.293 0.0000176539 ***
## condKnowledge:vignetteGerald -0.135357 0.143460 -0.944 0.34542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# linear updated exclusions
summary(linear_exclude[[20]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: luck_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * vignette
## Data: final_luck
##
## REML criterion at convergence: 115433.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.26629 -0.86904 0.00796 0.89563 2.77338
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 53.59 7.321
## vignette (Intercept) 467.01 21.610
## Residual 1333.31 36.514
## Number of obs: 11492, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 62.00486143107 21.79313778899
## compYes -2.82081270011 1.09714341861
## age 0.11252721496 0.04104859008
## gender2male -0.81708065650 0.77399510385
## education -0.19005123193 0.14129038984
## condIgnorance -1.45497177057 1.45511632391
## condKnowledge 12.69760114036 1.44027715415
## vignetteEmma -45.25132429500 30.64947387530
## vignetteGerald -20.59347789814 30.64900288394
## condIgnorance:vignetteEmma 41.98026886394 2.05257158723
## condKnowledge:vignetteEmma 13.96766969938 2.03635327821
## condIgnorance:vignetteGerald 15.92684810846 2.05659235562
## condKnowledge:vignetteGerald 3.62912065493 2.05084438817
## df t value Pr(>|t|)
## (Intercept) 0.00000009681 2.845 1.00000
## compYes 1795.38806869876 -2.571 0.01022 *
## age 7889.92575472046 2.741 0.00613 **
## gender2male 11472.40208757258 -1.056 0.29114
## education 10514.10400168045 -1.345 0.17862
## condIgnorance 11397.39945108335 -1.000 0.31738
## condKnowledge 11394.90458979245 8.816 < 2e-16 ***
## vignetteEmma 0.00000009468 -1.476 1.00000
## vignetteGerald 0.00000009467 -0.672 1.00000
## condIgnorance:vignetteEmma 11396.83241856327 20.453 < 2e-16 ***
## condKnowledge:vignetteEmma 11399.25746574652 6.859 0.0000000000072852 ***
## condIgnorance:vignetteGerald 11397.55369705422 7.744 0.0000000000000104 ***
## condKnowledge:vignetteGerald 11396.01539164708 1.770 0.07682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues# linear pre-registered exclusions
summary(linear_no[[20]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: luck_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * vignette
## Data: final_luck
##
## REML criterion at convergence: 74307.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.29618 -0.85176 -0.02675 0.89929 2.74315
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 50.03 7.073
## vignette (Intercept) 442.34 21.032
## Residual 1304.79 36.122
## Number of obs: 7417, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 56.603331862 21.326736960 0.000001706
## compYes -3.207545567 1.306773114 1417.222634030
## age 0.073279480 0.034777126 6377.409772372
## gender2male -0.415736991 0.941569560 7375.205914841
## education -0.019231662 0.184896502 6084.032672597
## condIgnorance 0.407572836 1.793742781 7365.416751346
## condKnowledge 17.784220887 1.759681574 7364.887326197
## vignetteEmma -39.286740994 29.900603963 0.000001648
## vignetteGerald -18.338432675 29.899410644 0.000001648
## condIgnorance:vignetteEmma 37.407950518 2.533560916 7367.772004482
## condKnowledge:vignetteEmma 6.332728477 2.500206749 7370.563665836
## condIgnorance:vignetteGerald 13.612039683 2.532708855 7365.178581504
## condKnowledge:vignetteGerald 0.414528408 2.523125338 7365.238043876
## t value Pr(>|t|)
## (Intercept) 2.654 1.0000
## compYes -2.455 0.0142 *
## age 2.107 0.0351 *
## gender2male -0.442 0.6588
## education -0.104 0.9172
## condIgnorance 0.227 0.8203
## condKnowledge 10.106 < 2e-16 ***
## vignetteEmma -1.314 1.0000
## vignetteGerald -0.613 1.0000
## condIgnorance:vignetteEmma 14.765 < 2e-16 ***
## condKnowledge:vignetteEmma 2.533 0.0113 *
## condIgnorance:vignetteGerald 5.374 0.0000000791 ***
## condKnowledge:vignetteGerald 0.164 0.8695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Turk
- Logistic versus linear: All models show no interaction of condition and Turk.
- Exclusions: All models show no interaction of condition and Turk.
View full results
# log updated exclusions
summary(log_exclude[[24]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7017.4 7115.5 -3495.7 6991.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5047 0.2054 0.2546 0.3087 0.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0023770476 0.0487550
## id:vignette (Intercept) 0.0000001472 0.0003837
## vignette (Intercept) 0.0768855931 0.2772825
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.073866 0.251554 8.244 < 2e-16 ***
## compYes 0.131776 0.075708 1.741 0.081757 .
## age -0.012466 0.003376 -3.692 0.000222 ***
## gender2male -0.232380 0.071089 -3.269 0.001080 **
## education 0.056843 0.011725 4.848 0.00000125 ***
## condIgnorance -0.378343 0.077451 -4.885 0.00000103 ***
## condKnowledge 0.440103 0.092314 4.767 0.00000187 ***
## turkTRUE 1.385038 0.368873 3.755 0.000173 ***
## condIgnorance:turkTRUE -0.655384 0.427927 -1.532 0.125638
## condKnowledge:turkTRUE -0.443704 0.514800 -0.862 0.388745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.251
## age -0.338 0.315
## gender2male -0.109 0.053 -0.039
## education -0.583 -0.094 -0.088 0.035
## condIgnornc -0.180 -0.003 0.005 0.004 0.001
## condKnowldg -0.154 0.002 0.001 -0.003 0.007 0.484
## turkTRUE 0.044 -0.113 -0.156 -0.050 -0.003 0.120 0.101
## cndIgn:TRUE 0.032 0.001 0.001 -0.002 -0.001 -0.181 -0.088 -0.835
## cndKnw:TRUE 0.027 0.000 0.001 0.001 -0.002 -0.087 -0.179 -0.694 0.598# log pre-registered exclusions
summary(log_no[[24]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 10140.0 10230.9 -5057.0 10114.0 8041
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2125 -0.8309 -0.4430 0.9046 2.4681
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.007294 0.08541
## id:vignette (Intercept) 0.005094 0.07137
## vignette (Intercept) 0.335650 0.57935
## Number of obs: 8054, groups:
## person_code:(id:vignette), 8054; id:vignette, 8054; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.384689 0.366283 -1.050 0.29360
## compYes -0.212691 0.056350 -3.774 0.00016 ***
## age 0.006544 0.002087 3.136 0.00172 **
## gender2male 0.104048 0.052069 1.998 0.04569 *
## education -0.028709 0.009843 -2.917 0.00354 **
## condIgnorance 1.070263 0.062995 16.990 < 2e-16 ***
## condKnowledge 1.022183 0.062783 16.281 < 2e-16 ***
## turkTRUE -0.267159 0.143859 -1.857 0.06330 .
## condIgnorance:turkTRUE -0.312009 0.190996 -1.634 0.10235
## condKnowledge:turkTRUE 0.081674 0.186521 0.438 0.66147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.082
## age -0.133 0.274
## gender2male -0.045 0.038 0.004
## education -0.335 -0.157 -0.096 -0.005
## condIgnornc -0.087 -0.016 0.018 0.014 -0.012
## condKnowldg -0.085 -0.018 0.016 0.007 -0.017 0.528
## turkTRUE 0.019 -0.194 -0.249 -0.096 0.002 0.223 0.225
## cndIgn:TRUE 0.028 0.006 -0.001 0.004 0.002 -0.326 -0.171 -0.687
## cndKnw:TRUE 0.029 0.008 0.003 -0.001 0.001 -0.175 -0.334 -0.705 0.529# linear updated exclusions
summary(linear_exclude[[21]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: luck_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * turk
## Data: final_luck
##
## REML criterion at convergence: 115879.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.20826 -0.82690 -0.05232 0.88579 2.41214
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 51.58 7.182
## vignette (Intercept) 174.34 13.204
## Residual 1384.81 37.213
## Number of obs: 11492, groups: person_code:vignette, 108; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.46249 8.03063 2.40926 5.039 0.0250 *
## compYes -2.62626 1.11565 1768.43733 -2.354 0.0187 *
## age 0.12229 0.04224 9701.27652 2.895 0.0038 **
## gender2male -0.50662 0.78911 11479.55357 -0.642 0.5209
## education -0.24391 0.14382 10370.91135 -1.696 0.0899 .
## condIgnorance 18.22654 0.89338 11400.06200 20.402 <2e-16 ***
## condKnowledge 18.32874 0.89151 11402.93140 20.559 <2e-16 ***
## turkTRUE -3.87830 4.77559 92.90025 -0.812 0.4188
## condIgnorance:turkTRUE -3.60810 3.04945 11382.24992 -1.183 0.2368
## condKnowledge:turkTRUE 2.84703 2.99023 11378.59470 0.952 0.3411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.113
## age -0.114 0.170
## gender2male -0.026 -0.002 -0.013
## education -0.233 -0.031 -0.111 0.002
## condIgnornc -0.057 0.002 0.007 0.000 0.000
## condKnowldg -0.056 0.004 0.001 -0.001 0.000 0.504
## turkTRUE 0.001 -0.087 -0.141 -0.042 0.010 0.093 0.094
## cndIgn:TRUE 0.017 -0.002 -0.010 0.003 0.002 -0.293 -0.148 -0.307
## cndKnw:TRUE 0.017 -0.001 0.001 0.000 -0.002 -0.150 -0.298 -0.314 0.492# linear pre-registered exclusions
summary(linear_no[[21]])## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: luck_vas ~ (1 | vignette/person_code) + comp + age + gender2 +
## education + cond * turk
## Data: final_luck
##
## REML criterion at convergence: 74574.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.13620 -0.82667 -0.06379 0.89004 2.44419
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:vignette (Intercept) 49.4 7.028
## vignette (Intercept) 151.0 12.288
## Residual 1349.7 36.738
## Number of obs: 7417, groups: person_code:vignette, 54; vignette, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 37.99730 7.75492 2.72402 4.900 0.0203 *
## compYes -2.80876 1.33188 1483.70966 -2.109 0.0351 *
## age 0.08733 0.03560 7110.29717 2.453 0.0142 *
## gender2male -0.04987 0.95833 7402.27863 -0.052 0.9585
## education -0.10224 0.18781 5969.67367 -0.544 0.5862
## condIgnorance 17.93814 1.12847 7368.87484 15.896 <2e-16 ***
## condKnowledge 19.76367 1.12575 7372.00590 17.556 <2e-16 ***
## turkTRUE -2.13558 4.76143 49.74337 -0.449 0.6557
## condIgnorance:turkTRUE -3.29727 3.09170 7361.03046 -1.066 0.2862
## condKnowledge:turkTRUE 1.40167 3.03440 7359.24962 0.462 0.6441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.122
## age -0.100 0.140
## gender2male -0.032 -0.004 0.014
## education -0.320 -0.041 -0.094 -0.010
## condIgnornc -0.074 0.002 0.004 0.001 0.002
## condKnowldg -0.074 0.003 -0.001 0.000 0.002 0.506
## turkTRUE -0.020 -0.097 -0.113 -0.055 0.002 0.118 0.119
## cndIgn:TRUE 0.027 -0.002 -0.008 0.003 0.001 -0.365 -0.185 -0.313
## cndKnw:TRUE 0.028 -0.001 0.001 -0.001 -0.004 -0.188 -0.371 -0.320 0.493Qualtrics versus SoSciSurvey
- This section examines if there is an interaction between data collection source (Qualtrics versus SoSciSurvey) and the results with condition.
table(final_long$source, useNA = "ifany")##
## Qualtrics SoSciSurvey
## 1668 12810Knowledge
- No interaction of source and condition results.
View full results
k.model.qualtrics <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*source,
data = final_long,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.model.qualtrics)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * source
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15873.7 15971.7 -7923.9 15847.7 13882
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8113 -0.6192 -0.4883 0.7791 4.0997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00000000000003787 0.0000001946
## id:vignette (Intercept) 0.00000000000000000 0.0000000000
## vignette (Intercept) 0.44757953395657191 0.6690138518
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.110683 0.419438 -0.264 0.7919
## compYes 0.008579 0.046544 0.184 0.8538
## age 0.002997 0.002200 1.362 0.1731
## gender2male -0.087356 0.042739 -2.044 0.0410 *
## education -0.016789 0.007530 -2.230 0.0258 *
## condIgnorance -1.478409 0.150831 -9.802 < 2e-16 ***
## condKnowledge 0.714284 0.131647 5.426 0.0000000577 ***
## sourceSoSciSurvey -0.056457 0.102949 -0.548 0.5834
## condIgnorance:sourceSoSciSurvey 0.186034 0.159820 1.164 0.2444
## condKnowledge:sourceSoSciSurvey -0.115661 0.139726 -0.828 0.4078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw srcSSS cI:SSS
## compYes -0.130
## age -0.182 0.292
## gender2male -0.044 0.034 -0.052
## education -0.216 -0.126 -0.123 0.032
## condIgnornc -0.135 0.000 -0.002 0.006 0.003
## condKnowldg -0.154 -0.001 0.000 -0.005 -0.002 0.428
## sorcSScSrvy -0.262 0.189 0.274 0.047 -0.030 0.549 0.629
## cndIgnr:SSS 0.127 0.001 0.001 -0.002 0.000 -0.943 -0.405 -0.585
## cndKnwl:SSS 0.145 0.001 0.000 0.003 0.000 -0.404 -0.941 -0.669 0.431
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Reasonable
- No interaction of source of data collection and condition.
View full results
r.model.qualtrics <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*source,
data = final_long,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.model.qualtrics)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * source
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7009.4 7107.5 -3491.7 6983.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.6066 0.2058 0.2557 0.3091 0.7091
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.001105542067 0.03324969
## id:vignette (Intercept) 0.000000001287 0.00003588
## vignette (Intercept) 0.076735814599 0.27701230
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.107859 0.368450 8.435 < 2e-16 ***
## compYes 0.108354 0.076479 1.417 0.15655
## age -0.013268 0.003389 -3.915 0.00009024 ***
## gender2male -0.209617 0.070832 -2.959 0.00308 **
## education 0.057681 0.011639 4.956 0.00000072 ***
## condIgnorance -0.801616 0.300334 -2.669 0.00761 **
## condKnowledge 0.546456 0.403795 1.353 0.17596
## sourceSoSciSurvey -1.035668 0.259899 -3.985 0.00006751 ***
## condIgnorance:sourceSoSciSurvey 0.428385 0.310483 1.380 0.16767
## condKnowledge:sourceSoSciSurvey -0.126980 0.414413 -0.306 0.75929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw srcSSS cI:SSS
## compYes -0.277
## age -0.365 0.333
## gender2male -0.094 0.041 -0.052
## education -0.395 -0.093 -0.086 0.037
## condIgnornc -0.553 0.002 0.004 0.001 0.000
## condKnowldg -0.411 0.002 0.003 0.002 0.000 0.503
## sorcSScSrvy -0.732 0.145 0.188 0.034 -0.001 0.782 0.582
## cndIgnr:SSS 0.534 -0.003 -0.003 -0.001 0.000 -0.967 -0.486 -0.800
## cndKnwl:SSS 0.400 -0.002 -0.003 -0.003 0.002 -0.490 -0.974 -0.600 0.502
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Luck
- No interaction of the survey programming with luck as the dependent variable.
View full results
l.model.qualtrics <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*source,
data = final_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.model.qualtrics)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * source
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15875.8 15972.6 -7924.9 15849.8 12627
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2714 -0.8127 -0.4484 0.9056 2.4708
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.006534 0.08083
## id:vignette (Intercept) 0.007099 0.08426
## vignette (Intercept) 0.373772 0.61137
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.693905 0.391717 -1.771 0.07649
## compYes -0.116423 0.045887 -2.537 0.01118
## age 0.008634 0.002178 3.965 0.000073309386957
## gender2male 0.019755 0.042317 0.467 0.64062
## education -0.033060 0.007507 -4.404 0.000010622832056
## condIgnorance 0.945502 0.144940 6.523 0.000000000068728
## condKnowledge 1.045767 0.141417 7.395 0.000000000000141
## sourceSoSciSurvey 0.351019 0.114721 3.060 0.00222
## condIgnorance:sourceSoSciSurvey 0.091837 0.153188 0.600 0.54884
## condKnowledge:sourceSoSciSurvey -0.122297 0.149856 -0.816 0.41445
##
## (Intercept) .
## compYes *
## age ***
## gender2male
## education ***
## condIgnorance ***
## condKnowledge ***
## sourceSoSciSurvey **
## condIgnorance:sourceSoSciSurvey
## condKnowledge:sourceSoSciSurvey
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw srcSSS cI:SSS
## compYes -0.137
## age -0.196 0.284
## gender2male -0.042 0.031 -0.052
## education -0.229 -0.127 -0.122 0.019
## condIgnornc -0.192 0.001 0.007 0.004 -0.003
## condKnowldg -0.197 0.002 0.011 -0.002 -0.006 0.530
## sorcSScSrvy -0.305 0.173 0.255 0.037 -0.032 0.654 0.671
## cndIgnr:SSS 0.182 -0.002 -0.001 -0.003 -0.002 -0.945 -0.501 -0.691
## cndKnwl:SSS 0.186 -0.005 -0.006 0.002 0.001 -0.499 -0.943 -0.707 0.528Table of Exclusions by Vignette and Condition
- Exclusions if one considered each comprehension check separately.
# 1 is right 2 is wrong for GK
# 1 is wrong 2 is right for I
full_long$correct <- NA
full_long$correct[full_long$cond == "Ignorance" & full_long$compr == "2"] <- TRUE
full_long$correct[full_long$cond == "Ignorance" & full_long$compr == "1"] <- FALSE
full_long$correct[full_long$cond != "Ignorance" & full_long$compr == "1"] <- TRUE
full_long$correct[full_long$cond != "Ignorance" & full_long$compr == "2"] <- FALSE
flextable(full_long %>%
group_by(vignette, cond, correct) %>%
select(vignette, cond, correct) %>%
drop_na() %>%
count())vignette | cond | correct | n |
|---|---|---|---|
Darrel | Gettier | FALSE | 344 |
Darrel | Gettier | TRUE | 2,176 |
Darrel | Ignorance | FALSE | 520 |
Darrel | Ignorance | TRUE | 2,301 |
Darrel | Knowledge | FALSE | 274 |
Darrel | Knowledge | TRUE | 2,364 |
Emma | Gettier | FALSE | 458 |
Emma | Gettier | TRUE | 2,201 |
Emma | Ignorance | FALSE | 375 |
Emma | Ignorance | TRUE | 2,324 |
Emma | Knowledge | FALSE | 359 |
Emma | Knowledge | TRUE | 2,285 |
Gerald | Gettier | FALSE | 676 |
Gerald | Gettier | TRUE | 2,130 |
Gerald | Ignorance | FALSE | 301 |
Gerald | Ignorance | TRUE | 2,184 |
Gerald | Knowledge | FALSE | 466 |
Gerald | Knowledge | TRUE | 2,243 |
Only Exclude For Condition Incorrect
- The first results presented in each section are analyses wherein each section excludes only the vignettes that were answered correctly, and the second summary is the original information presented in the manuscript.
# data prep
exclude_DF <- full_long %>%
filter(correct == TRUE) %>%
filter(age_exclusion == FALSE) %>%
filter(previous_exclusion == FALSE) %>%
filter(purpose_exclusion == FALSE) %>%
filter(lang_exclusion == FALSE)
nrow(exclude_DF)## [1] 18455length(unique(exclude_DF$id))## [1] 7049length(unique(exclude_DF$person_code))## [1] 37# full data
table(full_long$vignette, full_long$cond, useNA = "ifany")##
## Gettier Ignorance Knowledge
## Darrel 2821 3153 2972
## Emma 2982 3034 2930
## Gerald 3143 2759 3044
## <NA> 494 494 494# exclude based on incorrect answers
table(exclude_DF$vignette, exclude_DF$cond, useNA = "ifany")##
## Gettier Ignorance Knowledge
## Darrel 1986 2119 2174
## Emma 2009 2104 2085
## Gerald 1942 2001 2035# knowledge recode
exclude_DF$know_vas_binned <- exclude_DF$know_vas
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned <= 40] <- 2
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned > 40 &
exclude_DF$know_vas_binned < 60] <- NA
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned >= 60] <- 1
exclude_DF$know_vas_combined <- ifelse(is.na(exclude_DF$know_vas_binned),
exclude_DF$know_bin,
exclude_DF$know_vas_binned)
exclude_DF$know_vas_combined <- 3 - exclude_DF$know_vas_combined
# reason recode
exclude_DF$reason_vas_binned <- exclude_DF$reason_vas
exclude_DF$reason_vas_binned[exclude_DF$reason_vas_binned <= 40] <- 2
exclude_DF$reason_vas_binned[exclude_DF$reason_vas_binned > 40 &
exclude_DF$reason_vas_binned < 60] <- NA
exclude_DF$reason_vas_binned[exclude_DF$reason_vas_binned >= 60] <- 1
exclude_DF$reason_vas_combined <- ifelse(is.na(exclude_DF$reason_vas_binned),
exclude_DF$reason_bin,
exclude_DF$reason_vas_binned)
exclude_DF$reason_vas_combined <- 3 - exclude_DF$reason_vas_combined
# luck recode
exclude_DF$luck_vas_binned <- exclude_DF$luck_vas
exclude_DF$luck_vas_binned[exclude_DF$luck_vas_binned <= 40] <- 2
exclude_DF$luck_vas_binned[exclude_DF$luck_vas_binned > 40 &
exclude_DF$luck_vas_binned < 60] <- NA
exclude_DF$luck_vas_binned[exclude_DF$luck_vas_binned >= 60] <- 1
exclude_DF$luck_vas_combined <- ifelse(is.na(exclude_DF$luck_vas_binned),
exclude_DF$luck_bin,
exclude_DF$luck_vas_binned)
exclude_DF$luck_vas_combined <- 3 - exclude_DF$luck_vas_combined
# for luck analyses people should be excluded if they get the answer wrong
exclude_DF$luck_correct <- FALSE
exclude_DF$ri_wr <- factor(exclude_DF$ri_wr,
levels = c(1,2),
labels = c("Right", "Wrong"))
exclude_DF$luck_correct[exclude_DF$cond == "Ignorance" & exclude_DF$ri_wr == "Wrong"] <- TRUE
exclude_DF$luck_correct[exclude_DF$cond != "Ignorance" & exclude_DF$ri_wr == "Right"] <- TRUE
table(exclude_DF$luck_correct)##
## FALSE TRUE
## 1473 16982# fix other variables
exclude_DF$gender2 <- factor(exclude_DF$gender,
levels = c("female", "male"))
# subset the wrong answers
exclude_luck <- subset(exclude_DF, luck_correct)Knowledge
Condition
- Results from coefficients are within a confidence interval of each other.
View full results
exclude_DF$know_vas_combined <- exclude_DF$know_vas_combined - 1
k.cond.exclude <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.cond.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 20472.8 20550.6 -10226.4 20452.8 17617
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7573 -0.6379 -0.3242 0.7773 3.5460
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000 0.0000
## id:vignette (Intercept) 0.0000 0.0000
## vignette (Intercept) 0.3965 0.6297
## Number of obs: 17627, groups:
## person_code:(id:vignette), 17627; id:vignette, 17627; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.193853 0.378270 -0.512 0.6083
## compYes 0.023915 0.039203 0.610 0.5418
## age 0.004261 0.001747 2.439 0.0147 *
## gender2male -0.057121 0.037166 -1.537 0.1243
## education -0.013869 0.006537 -2.122 0.0339 *
## condIgnorance -1.261018 0.043885 -28.735 <2e-16 ***
## condKnowledge 0.585633 0.039344 14.885 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.073
## age -0.104 0.210
## gender2male -0.028 0.013 -0.090
## education -0.219 -0.113 -0.102 0.036
## condIgnornc -0.049 -0.005 0.005 0.006 0.007
## condKnowldg -0.052 0.004 0.005 -0.012 -0.009 0.450
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[6]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15872.0 15947.4 -7926.0 15852.0 13885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7728 -0.6220 -0.4902 0.7726 3.8696
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000 0.0000
## id:vignette (Intercept) 0.0000 0.0000
## vignette (Intercept) 0.4474 0.6689
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.183810 0.404093 -0.455 0.6492
## compYes 0.019364 0.044633 0.434 0.6644
## age 0.003733 0.001997 1.870 0.0615 .
## gender2male -0.084539 0.042604 -1.984 0.0472 *
## education -0.017069 0.007528 -2.267 0.0234 *
## condIgnorance -1.313222 0.050238 -26.140 <2e-16 ***
## condKnowledge 0.611665 0.044435 13.765 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.075
## age -0.107 0.197
## gender2male -0.030 0.014 -0.090
## education -0.235 -0.118 -0.115 0.035
## condIgnornc -0.049 0.001 -0.007 0.011 0.008
## condKnowldg -0.054 0.001 0.002 -0.006 -0.005 0.431
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Vignette
- Results from coefficients are within a confidence interval of each other.
View full results
k.vignette.exclude <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*vignette,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.vignette.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 20394.5 20518.9 -10181.3 20362.5 17611
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7656 -0.5950 -0.3998 0.7729 2.8762
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00000000002716 0.000005211
## id:vignette (Intercept) 0.00000001285080 0.000113361
## vignette (Intercept) 0.00000000000000 0.000000000
## Number of obs: 17627, groups:
## person_code:(id:vignette), 17627; id:vignette, 17627; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.519592 0.111542 4.658 3.19e-06 ***
## compYes 0.023380 0.039387 0.594 0.552775
## age 0.004292 0.001754 2.447 0.014414 *
## gender2male -0.059702 0.037333 -1.599 0.109784
## education -0.013795 0.006568 -2.100 0.035701 *
## condIgnorance -1.516116 0.069414 -21.842 < 2e-16 ***
## condKnowledge 0.479315 0.067636 7.087 1.37e-12 ***
## vignetteEmma -1.820538 0.074033 -24.591 < 2e-16 ***
## vignetteGerald -0.388143 0.066356 -5.849 4.93e-09 ***
## condIgnorance:vignetteEmma 0.912496 0.112782 8.091 5.93e-16 ***
## condKnowledge:vignetteEmma 0.340920 0.100124 3.405 0.000662 ***
## condIgnorance:vignetteGerald 0.132929 0.100792 1.319 0.187223
## condKnowledge:vignetteGerald 0.045013 0.094871 0.474 0.635168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[7]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15807.7 15928.3 -7887.8 15775.7 13879
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7804 -0.5717 -0.4484 0.7819 3.1287
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000172891 0.0000041580
## id:vignette (Intercept) 0.0000000000007192 0.0000008481
## vignette (Intercept) 0.0000000000000000 0.0000000000
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.555020 0.126409 4.391 0.000011300772713 ***
## compYes 0.016780 0.044844 0.374 0.708268
## age 0.003778 0.002004 1.886 0.059360 .
## gender2male -0.087674 0.042794 -2.049 0.040489 *
## education -0.016600 0.007561 -2.196 0.028122 *
## condIgnorance -1.604292 0.080023 -20.048 < 2e-16 ***
## condKnowledge 0.503172 0.076198 6.603 0.000000000040161 ***
## vignetteEmma -1.930434 0.084439 -22.862 < 2e-16 ***
## vignetteGerald -0.390320 0.073064 -5.342 0.000000091833949 ***
## condIgnorance:vignetteEmma 0.977499 0.132791 7.361 0.000000000000182 ***
## condKnowledge:vignetteEmma 0.395026 0.114528 3.449 0.000562 ***
## condIgnorance:vignetteGerald 0.223192 0.113776 1.962 0.049800 *
## condKnowledge:vignetteGerald 0.021780 0.106168 0.205 0.837455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Turk
- Results from coefficients are within a confidence interval of each other. The significance level changes for the Turk analysis, however, this is likely due to power, as there are more participants in this analysis.
- The pattern of data suggests the same results, with more selection of believes in the ignorance condition.
View full results
k.turk.exclude <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*turk,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.turk.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 20428.9 20530.0 -10201.4 20402.9 17614
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1976 -0.6499 -0.3169 0.7909 3.6839
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000001772 0.000000421
## id:vignette (Intercept) 0.0000000000000000 0.000000000
## vignette (Intercept) 0.3980771543788082 0.630933558
## Number of obs: 17627, groups:
## person_code:(id:vignette), 17627; id:vignette, 17627; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0734328 0.3796903 -0.193 0.846644
## compYes -0.0354153 0.0406158 -0.872 0.383231
## age 0.0002084 0.0019163 0.109 0.913387
## gender2male -0.0894413 0.0377330 -2.370 0.017770 *
## education -0.0137125 0.0065381 -2.097 0.035964 *
## condIgnorance -1.2265206 0.0454744 -26.972 < 2e-16 ***
## condKnowledge 0.5681281 0.0408046 13.923 < 2e-16 ***
## turkTRUE 0.4431109 0.1154278 3.839 0.000124 ***
## condIgnorance:turkTRUE -0.5137547 0.1738963 -2.954 0.003133 **
## condKnowledge:turkTRUE 0.2523092 0.1570769 1.606 0.108213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.086
## age -0.119 0.292
## gender2male -0.037 0.053 -0.022
## education -0.218 -0.111 -0.097 0.035
## condIgnornc -0.052 0.001 0.012 0.009 0.005
## condKnowldg -0.053 0.003 0.000 -0.010 -0.010 0.452
## turkTRUE 0.028 -0.173 -0.260 -0.097 0.001 0.158 0.182
## cndIgn:TRUE 0.011 0.002 0.005 0.001 0.004 -0.259 -0.120 -0.607
## cndKnw:TRUE 0.012 0.002 0.012 -0.004 0.002 -0.120 -0.258 -0.673 0.445
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[8]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15850.2 15948.2 -7912.1 15824.2 13882
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1117 -0.6244 -0.4852 0.7822 3.9470
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000002108 0.0000004591
## id:vignette (Intercept) 0.0000000000024070 0.0000015515
## vignette (Intercept) 0.4494139896495767 0.6703834646
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0743861 0.4058531 -0.183 0.85458
## compYes -0.0330383 0.0463320 -0.713 0.47580
## age -0.0001138 0.0022169 -0.051 0.95906
## gender2male -0.1113136 0.0431663 -2.579 0.00992 **
## education -0.0164575 0.0075305 -2.185 0.02886 *
## condIgnorance -1.2894721 0.0521660 -24.719 < 2e-16 ***
## condKnowledge 0.5914056 0.0461318 12.820 < 2e-16 ***
## turkTRUE 0.3226120 0.1260957 2.558 0.01051 *
## condIgnorance:turkTRUE -0.3324922 0.1930846 -1.722 0.08507 .
## condKnowledge:turkTRUE 0.2986885 0.1735316 1.721 0.08521 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.090
## age -0.124 0.288
## gender2male -0.039 0.053 -0.021
## education -0.233 -0.119 -0.112 0.032
## condIgnornc -0.052 0.006 0.001 0.011 0.007
## condKnowldg -0.055 -0.001 -0.002 -0.006 -0.005 0.433
## turkTRUE 0.031 -0.182 -0.282 -0.098 0.011 0.158 0.184
## cndIgn:TRUE 0.013 -0.001 0.001 0.007 0.001 -0.268 -0.119 -0.588
## cndKnw:TRUE 0.014 0.000 0.002 -0.001 0.002 -0.118 -0.263 -0.654 0.427
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')data_graph <- k.turk.exclude@frame
data_graph$know_vas_combined <- factor(data_graph$know_vas_combined,
levels = c(0,1),
labels = c("Believes", "Knows"))
#graph the three way binary
ggplot(data_graph) +
geom_mosaic(aes(x = product(know_vas_combined, cond, turk),
fill = know_vas_combined), color = "black", size = .5) +
scale_fill_brewer(palette = "Greys", name = "Knowledge Choice",
direction = -1) +
theme(text = element_text(size = 15)) +
scale_x_productlist(breaks = c(.5,.95),
labels = c("College", "MTurk")) +
theme_classic() +
xlab("Turk") +
ylab("Condition")## Warning: `unite_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `unite()` instead.
## ℹ The deprecated feature was likely used in the ggmosaic package.
## Please report the issue at <]8;;https://github.com/haleyjeppson/ggmosaichttps://github.com/haleyjeppson/ggmosaic]8;;>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Reasonable
Condition
- Results from coefficients are within a confidence interval of each other.
View full results
exclude_DF$reason_vas_combined <- exclude_DF$reason_vas_combined - 1
r.cond.exclude <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.cond.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9517.7 9595.6 -4748.9 9497.7 17719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0153 0.2255 0.2701 0.3203 0.6333
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.005945 0.07711
## id:vignette (Intercept) 0.000840 0.02898
## vignette (Intercept) 0.053351 0.23098
## Number of obs: 17729, groups:
## person_code:(id:vignette), 17729; id:vignette, 17729; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.732079 0.210919 8.212 < 2e-16 ***
## compYes 0.269602 0.061981 4.350 0.0000136278 ***
## age -0.003187 0.002776 -1.148 0.251052
## gender2male -0.211450 0.059866 -3.532 0.000412 ***
## education 0.053084 0.010104 5.254 0.0000001489 ***
## condIgnorance -0.342278 0.065361 -5.237 0.0000001634 ***
## condKnowledge 0.410338 0.076235 5.383 0.0000000734 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.210
## age -0.313 0.210
## gender2male -0.088 0.012 -0.093
## education -0.603 -0.091 -0.085 0.032
## condIgnornc -0.182 -0.009 0.016 0.001 -0.002
## condKnowldg -0.154 0.006 0.002 -0.010 -0.001 0.496summary(log_exclude[[14]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7047.1 7122.6 -3513.6 7027.1 13964
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6491 0.2093 0.2544 0.3073 0.5823
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.001461 0.03822
## id:vignette (Intercept) 0.007296 0.08542
## vignette (Intercept) 0.076656 0.27687
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.839819 0.248928 7.391 0.000000000000146 ***
## compYes 0.251565 0.072504 3.470 0.000521 ***
## age -0.004968 0.003209 -1.548 0.121581
## gender2male -0.181153 0.070632 -2.565 0.010326 *
## education 0.057713 0.011926 4.839 0.000001303215506 ***
## condIgnorance -0.400271 0.075940 -5.271 0.000000135772713 ***
## condKnowledge 0.425613 0.090671 4.694 0.000002678579960 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.202
## age -0.297 0.201
## gender2male -0.085 0.014 -0.095
## education -0.599 -0.098 -0.097 0.030
## condIgnornc -0.178 -0.005 0.002 0.002 0.001
## condKnowldg -0.154 0.003 0.003 -0.002 0.007 0.487Vignette
- Results from coefficients are within a confidence interval of each other.
View full results
r.vignette.exclude <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*vignette,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.vignette.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * vignette
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9493.4 9617.9 -4730.7 9461.4 17713
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3289 0.2153 0.2696 0.3289 0.6023
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 3.170e-03 0.05630464413
## id:vignette (Intercept) 3.097e-06 0.00175968972
## vignette (Intercept) 5.124e-15 0.00000007158
## Number of obs: 17729, groups:
## person_code:(id:vignette), 17729; id:vignette, 17729; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.274835 0.189689 11.992 < 2e-16
## compYes 0.268822 0.062029 4.334 0.0000146542217458
## age -0.003160 0.002781 -1.136 0.255933
## gender2male -0.212684 0.059901 -3.551 0.000384
## education 0.053685 0.010113 5.309 0.0000001104631506
## condIgnorance -0.770139 0.134068 -5.744 0.0000000092242423
## condKnowledge 0.243419 0.161056 1.511 0.130688
## vignetteEmma -1.008890 0.131325 -7.682 0.0000000000000156
## vignetteGerald -0.493570 0.141754 -3.482 0.000498
## condIgnorance:vignetteEmma 0.733331 0.167123 4.388 0.0000114417360044
## condKnowledge:vignetteEmma 0.416902 0.199190 2.093 0.036350
## condIgnorance:vignetteGerald 0.358636 0.177623 2.019 0.043478
## condKnowledge:vignetteGerald -0.046783 0.207371 -0.226 0.821512
##
## (Intercept) ***
## compYes ***
## age
## gender2male ***
## education ***
## condIgnorance ***
## condKnowledge
## vignetteEmma ***
## vignetteGerald ***
## condIgnorance:vignetteEmma ***
## condKnowledge:vignetteEmma *
## condIgnorance:vignetteGerald *
## condKnowledge:vignetteGerald
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[15]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7025.8 7146.5 -3496.9 6993.8 13958
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4722 0.2020 0.2562 0.3173 0.5561
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.000000 0.00000
## id:vignette (Intercept) 0.002124 0.04609
## vignette (Intercept) 0.000000 0.00000
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.425110 0.222940 10.878 < 2e-16 ***
## compYes 0.248903 0.072555 3.431 0.000602 ***
## age -0.005006 0.003218 -1.556 0.119764
## gender2male -0.181886 0.070666 -2.574 0.010056 *
## education 0.058594 0.011936 4.909 0.0000009160223 ***
## condIgnorance -0.863705 0.160582 -5.379 0.0000000750731 ***
## condKnowledge 0.390875 0.204642 1.910 0.056127 .
## vignetteEmma -1.099565 0.155615 -7.066 0.0000000000016 ***
## vignetteGerald -0.522306 0.168276 -3.104 0.001910 **
## condIgnorance:vignetteEmma 0.741195 0.196886 3.765 0.000167 ***
## condKnowledge:vignetteEmma 0.256617 0.245839 1.044 0.296559
## condIgnorance:vignetteGerald 0.424379 0.210062 2.020 0.043357 *
## condKnowledge:vignetteGerald -0.237181 0.255828 -0.927 0.353869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Turk
- Results from coefficients are within a confidence interval of each other. The significance level changes for the Turk analysis, however, this is likely due to power, as there are more participants in this analysis.
- This pattern of results indicates the same results as other samples - very few people pick unreasonable overall with slightly more in the ignorance category.
View full results
r.turk.exclude <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*turk,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.turk.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 9473.4 9574.6 -4723.7 9447.4 17716
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.9148 0.2232 0.2714 0.3219 0.6611
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00275884 0.05252
## id:vignette (Intercept) 0.00001282 0.00358
## vignette (Intercept) 0.05311902 0.23048
## Number of obs: 17729, groups:
## person_code:(id:vignette), 17729; id:vignette, 17729; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.955658 0.212975 9.183 < 2e-16 ***
## compYes 0.157029 0.064484 2.435 0.0149 *
## age -0.010047 0.002887 -3.481 0.0005 ***
## gender2male -0.264650 0.060312 -4.388 0.000011437 ***
## education 0.051578 0.009951 5.183 0.000000218 ***
## condIgnorance -0.314020 0.066613 -4.714 0.000002428 ***
## condKnowledge 0.415941 0.077366 5.376 0.000000076 ***
## turkTRUE 1.500966 0.345523 4.344 0.000013988 ***
## condIgnorance:turkTRUE -0.916342 0.391519 -2.340 0.0193 *
## condKnowledge:turkTRUE -0.310357 0.483036 -0.643 0.5205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.258
## age -0.352 0.312
## gender2male -0.115 0.053 -0.035
## education -0.592 -0.084 -0.071 0.038
## condIgnornc -0.183 -0.006 0.019 0.004 -0.003
## condKnowldg -0.155 0.006 0.001 -0.009 0.000 0.492
## turkTRUE 0.042 -0.101 -0.137 -0.048 -0.008 0.108 0.095
## cndIgn:TRUE 0.029 0.002 0.001 -0.005 0.002 -0.170 -0.084 -0.860
## cndKnw:TRUE 0.023 0.001 0.006 -0.001 -0.001 -0.079 -0.160 -0.698 0.615summary(log_exclude[[16]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7017.4 7115.5 -3495.7 6991.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5047 0.2054 0.2546 0.3087 0.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0023770476 0.0487550
## id:vignette (Intercept) 0.0000001472 0.0003837
## vignette (Intercept) 0.0768855931 0.2772825
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.073866 0.251554 8.244 < 2e-16 ***
## compYes 0.131776 0.075708 1.741 0.081757 .
## age -0.012466 0.003376 -3.692 0.000222 ***
## gender2male -0.232380 0.071089 -3.269 0.001080 **
## education 0.056843 0.011725 4.848 0.00000125 ***
## condIgnorance -0.378343 0.077451 -4.885 0.00000103 ***
## condKnowledge 0.440103 0.092314 4.767 0.00000187 ***
## turkTRUE 1.385038 0.368873 3.755 0.000173 ***
## condIgnorance:turkTRUE -0.655384 0.427927 -1.532 0.125638
## condKnowledge:turkTRUE -0.443704 0.514800 -0.862 0.388745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.251
## age -0.338 0.315
## gender2male -0.109 0.053 -0.039
## education -0.583 -0.094 -0.088 0.035
## condIgnornc -0.180 -0.003 0.005 0.004 0.001
## condKnowldg -0.154 0.002 0.001 -0.003 0.007 0.484
## turkTRUE 0.044 -0.113 -0.156 -0.050 -0.003 0.120 0.101
## cndIgn:TRUE 0.032 0.001 0.001 -0.002 -0.001 -0.181 -0.088 -0.835
## cndKnw:TRUE 0.027 0.000 0.001 0.001 -0.002 -0.087 -0.179 -0.694 0.598data_graph <- r.turk.exclude@frame
data_graph$reason_vas_combined <- factor(data_graph$reason_vas_combined,
levels = c(0,1),
labels = c("Unreasonable", "Reasonable"))
#graph the three way binary
ggplot(data_graph) +
geom_mosaic(aes(x = product(reason_vas_combined, cond, turk),
fill = reason_vas_combined), color = "black", size = .5) +
scale_fill_brewer(palette = "Greys", name = "Reason Choice",
direction = -1) +
theme(text = element_text(size = 15)) +
scale_x_productlist(breaks = c(.5,.95),
labels = c("College", "MTurk")) +
theme_classic() +
xlab("Turk") +
ylab("Condition")
Luck
Condition
- Results from coefficients are within a confidence interval of each other.
View full results
exclude_luck$luck_vas_combined <- exclude_luck$luck_vas_combined - 1
l.cond.exclude <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond,
data = exclude_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.cond.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: exclude_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 20023.7 20100.4 -10001.8 20003.7 15862
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1927 -0.8876 0.4476 0.8101 2.1873
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0092245 0.09604
## id:vignette (Intercept) 0.0009849 0.03138
## vignette (Intercept) 0.3531817 0.59429
## Number of obs: 15872, groups:
## person_code:(id:vignette), 15872; id:vignette, 15872; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.196230 0.358969 0.547 0.58462
## compYes 0.155525 0.039163 3.971 0.000071505 ***
## age -0.004576 0.001756 -2.606 0.00915 **
## gender2male 0.029631 0.037335 0.794 0.42740
## education 0.032963 0.006592 5.001 0.000000572 ***
## condIgnorance -0.991065 0.042358 -23.397 < 2e-16 ***
## condKnowledge -0.945551 0.042021 -22.502 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.076
## age -0.111 0.203
## gender2male -0.027 0.011 -0.089
## education -0.232 -0.110 -0.100 0.025
## condIgnornc -0.061 -0.008 0.032 -0.002 -0.016
## condKnowldg -0.057 -0.010 0.011 -0.010 -0.021 0.533summary(log_exclude[[22]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15896.2 15970.6 -7938.1 15876.2 12630
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1575 -0.8039 -0.4574 0.9076 2.2438
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.009841 0.09920
## id:vignette (Intercept) 0.004391 0.06626
## vignette (Intercept) 0.373469 0.61112
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.246316 0.372248 -0.662 0.5082
## compYes -0.180694 0.043976 -4.109 0.0000398 ***
## age 0.004153 0.001967 2.111 0.0348 *
## gender2male 0.007267 0.042185 0.172 0.8632
## education -0.031096 0.007485 -4.155 0.0000326 ***
## condIgnorance 1.027734 0.047414 21.676 < 2e-16 ***
## condKnowledge 0.935679 0.047106 19.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.079
## age -0.116 0.191
## gender2male -0.029 0.014 -0.086
## education -0.253 -0.116 -0.109 0.023
## condIgnornc -0.064 -0.007 0.018 0.002 -0.014
## condKnowldg -0.062 -0.012 0.009 -0.001 -0.017 0.525Vignette
- Results from coefficients are within a confidence interval of each other.
View full results
l.vignette.exclude <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*vignette,
data = exclude_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.vignette.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: exclude_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 19522.3 19645.0 -9745.1 19490.3 15856
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4178 -0.8420 0.2872 1.0139 2.3088
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 4.840e-10 2.200e-05
## id:vignette (Intercept) 7.903e-04 2.811e-02
## vignette (Intercept) 8.701e-51 9.328e-26
## Number of obs: 15872, groups:
## person_code:(id:vignette), 15872; id:vignette, 15872; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.938281 0.114892 -8.167 3.17e-16 ***
## compYes 0.161667 0.039751 4.067 4.76e-05 ***
## age -0.004615 0.001790 -2.578 0.00993 **
## gender2male 0.039365 0.037780 1.042 0.29743
## education 0.033920 0.006705 5.059 4.22e-07 ***
## condIgnorance -0.055473 0.069668 -0.796 0.42589
## condKnowledge -0.726025 0.073752 -9.844 < 2e-16 ***
## vignetteEmma 2.711605 0.095022 28.537 < 2e-16 ***
## vignetteGerald 1.040804 0.071725 14.511 < 2e-16 ***
## condIgnorance:vignetteEmma -2.367220 0.116687 -20.287 < 2e-16 ***
## condKnowledge:vignetteEmma -0.957005 0.119734 -7.993 1.32e-15 ***
## condIgnorance:vignetteGerald -0.775527 0.099593 -7.787 6.86e-15 ***
## condKnowledge:vignetteGerald -0.137310 0.102304 -1.342 0.17954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[23]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15458.4 15577.5 -7713.2 15426.4 12624
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1894 -1.0118 -0.2784 0.8435 3.7019
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0004629 0.02151
## id:vignette (Intercept) 0.0015660 0.03957
## vignette (Intercept) 0.0000000 0.00000
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.907304 0.128577 7.056 0.0000000000017077
## compYes -0.195308 0.044696 -4.370 0.0000124396016865
## age 0.004152 0.002002 2.074 0.0381
## gender2male -0.004211 0.042725 -0.099 0.9215
## education -0.031082 0.007607 -4.086 0.0000439445273061
## condIgnorance 0.083929 0.078418 1.070 0.2845
## condKnowledge 0.723553 0.082462 8.774 < 2e-16
## vignetteEmma -2.851015 0.110619 -25.773 < 2e-16
## vignetteGerald -1.070444 0.078819 -13.581 < 2e-16
## condIgnorance:vignetteEmma 2.527798 0.135060 18.716 < 2e-16
## condKnowledge:vignetteEmma 1.067342 0.138234 7.721 0.0000000000000115
## condIgnorance:vignetteGerald 0.723264 0.110503 6.545 0.0000000000594091
## condKnowledge:vignetteGerald 0.094904 0.113794 0.834 0.4043
##
## (Intercept) ***
## compYes ***
## age *
## gender2male
## education ***
## condIgnorance
## condKnowledge ***
## vignetteEmma ***
## vignetteGerald ***
## condIgnorance:vignetteEmma ***
## condKnowledge:vignetteEmma ***
## condIgnorance:vignetteGerald ***
## condKnowledge:vignetteGerald
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Turk
- Results from coefficients are within a confidence interval of each other.
View full results
l.turk.exclude <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*turk,
data = exclude_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.turk.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: exclude_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 19990.1 20089.8 -9982.1 19964.1 15859
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4762 -0.8825 0.3890 0.8178 2.2976
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.006357 0.07973
## id:vignette (Intercept) 0.003636 0.06030
## vignette (Intercept) 0.353443 0.59451
## Number of obs: 15872, groups:
## person_code:(id:vignette), 15872; id:vignette, 15872; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.331809 0.359922 0.922 0.356586
## compYes 0.091648 0.040658 2.254 0.024188 *
## age -0.009262 0.001936 -4.784 0.000001717 ***
## gender2male -0.002261 0.037795 -0.060 0.952305
## education 0.033580 0.006603 5.085 0.000000367 ***
## condIgnorance -1.007100 0.043863 -22.960 < 2e-16 ***
## condKnowledge -0.937813 0.043576 -21.521 < 2e-16 ***
## turkTRUE 0.430552 0.129627 3.321 0.000895 ***
## condIgnorance:turkTRUE 0.207977 0.171212 1.215 0.224468
## condKnowledge:turkTRUE -0.144527 0.165501 -0.873 0.382516
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.090
## age -0.127 0.286
## gender2male -0.035 0.048 -0.021
## education -0.231 -0.111 -0.098 0.022
## condIgnornc -0.064 -0.004 0.035 0.001 -0.017
## condKnowldg -0.060 -0.005 0.015 -0.005 -0.021 0.531
## turkTRUE 0.025 -0.160 -0.250 -0.082 0.006 0.167 0.174
## cndIgn:TRUE 0.014 0.004 0.005 0.001 0.006 -0.253 -0.133 -0.700
## cndKnw:TRUE 0.014 0.007 0.016 -0.004 0.002 -0.137 -0.261 -0.726 0.547summary(log_exclude[[24]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7017.4 7115.5 -3495.7 6991.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5047 0.2054 0.2546 0.3087 0.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0023770476 0.0487550
## id:vignette (Intercept) 0.0000001472 0.0003837
## vignette (Intercept) 0.0768855931 0.2772825
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.073866 0.251554 8.244 < 2e-16 ***
## compYes 0.131776 0.075708 1.741 0.081757 .
## age -0.012466 0.003376 -3.692 0.000222 ***
## gender2male -0.232380 0.071089 -3.269 0.001080 **
## education 0.056843 0.011725 4.848 0.00000125 ***
## condIgnorance -0.378343 0.077451 -4.885 0.00000103 ***
## condKnowledge 0.440103 0.092314 4.767 0.00000187 ***
## turkTRUE 1.385038 0.368873 3.755 0.000173 ***
## condIgnorance:turkTRUE -0.655384 0.427927 -1.532 0.125638
## condKnowledge:turkTRUE -0.443704 0.514800 -0.862 0.388745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.251
## age -0.338 0.315
## gender2male -0.109 0.053 -0.039
## education -0.583 -0.094 -0.088 0.035
## condIgnornc -0.180 -0.003 0.005 0.004 0.001
## condKnowldg -0.154 0.002 0.001 -0.003 0.007 0.484
## turkTRUE 0.044 -0.113 -0.156 -0.050 -0.003 0.120 0.101
## cndIgn:TRUE 0.032 0.001 0.001 -0.002 -0.001 -0.181 -0.088 -0.835
## cndKnw:TRUE 0.027 0.000 0.001 0.001 -0.002 -0.087 -0.179 -0.694 0.598Just Emma or Gerald
- This second covers the “Direct” replication analysis on just Emma and just Gerald, given they correctly answered the question for that vignette correctly (the scoring for correct is presented earlier in this document).
# data prep
exclude_DF <- full_long %>%
filter(correct == TRUE)
# knowledge recode
exclude_DF$know_vas_binned <- exclude_DF$know_vas
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned <= 40] <- 2
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned > 40 &
exclude_DF$know_vas_binned < 60] <- NA
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned >= 60] <- 1
exclude_DF$know_vas_combined <- ifelse(is.na(exclude_DF$know_vas_binned),
exclude_DF$know_bin,
exclude_DF$know_vas_binned)
exclude_DF$know_vas_combined <- 3 - exclude_DF$know_vas_combined
# fix other variables
exclude_DF$gender2 <- factor(exclude_DF$gender,
levels = c("female", "male"))
# gerald
gerald_DF <- exclude_DF %>%
filter(vignette_order == "GED" | vignette_order == "GDE") %>%
filter(vignette == "Gerald")
nrow(gerald_DF)## [1] 2203# emma
emma_DF <- exclude_DF %>%
filter(vignette_order == "EDG" | vignette_order == "EGD") %>%
filter(vignette == "Emma")
nrow(emma_DF)## [1] 2295Gerald
- Overall chi-square is significant with a similar effect size to Darrel only.
- Gettier versus Ignorance shows the same effect size and pattern of effects.
- Gettier versus Knowledge shows the same effect size and pattern of
effects.
View full results
gerald_only <- chisq.test(gerald_DF$cond, gerald_DF$know_vas_combined)
MOTE_gerald <- v.chi.sq(
x2 = gerald_only$statistic,
n = sum(gerald_only$observed),
r = 2,
c = 3,
a = .05
)
gerald_only##
## Pearson's Chi-squared test
##
## data: gerald_DF$cond and gerald_DF$know_vas_combined
## X-squared = 211.49, df = 2, p-value < 2.2e-16MOTE_gerald## $v
## X-squared
## 0.3148861
##
## $vlow
## [1] 0.2733672
##
## $vhigh
## [1] 0.3579365
##
## $n
## [1] 2133
##
## $df
## [1] 2
##
## $x2
## X-squared
## 211.4939
##
## $p
## X-squared
## 1.187641e-46
##
## $estimate
## [1] "$V$ = .31, 95\\% CI [.27, .36]"
##
## $statistic
## [1] "$\\chi^2$(2) = 211.49, $p$ < .001"k.g_table_rep <- table(gerald_DF$know_vas_combined, gerald_DF$cond)
k.g_table_rep##
## Gettier Ignorance Knowledge
## 1 377 541 274
## 2 301 179 461k.gcond_GI_rep <- prop.test(t(k.g_table_rep[2:1, 1:2])) %>%
tidy()
k.gcond_GI_rep## # A tibble: 1 × 9
## estimate1 estimate2 statistic p.value parame…¹ conf.…² conf.…³ method alter…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 0.444 0.249 58.2 2.33e-14 1 0.145 0.246 2-sam… two.si…
## # … with abbreviated variable names ¹parameter, ²conf.low, ³conf.high,
## # ⁴alternativek.gcond_GI_v_rep <- v.chi.sq(x2 = prop.test(t(k.g_table_rep[2:1, 1:2]))$statistic,
n = sum(t(k.g_table_rep[2:1, 1:2])),
r = 2, c = 2)
k.gcond_GI_v_rep## $v
## X-squared
## 0.2040981
##
## $vlow
## [1] 0.1540183
##
## $vhigh
## [1] 0.2579082
##
## $n
## [1] 1398
##
## $df
## [1] 1
##
## $x2
## X-squared
## 58.23511
##
## $p
## X-squared
## 0.0000000000000232591
##
## $estimate
## [1] "$V$ = .20, 95\\% CI [.15, .26]"
##
## $statistic
## [1] "$\\chi^2$(1) = 58.24, $p$ < .001"k.gcond_GK_rep <- prop.test(t(k.g_table_rep[2:1, c(1,3)])) %>%
tidy()
k.gcond_GK_rep## # A tibble: 1 × 9
## estimate1 estimate2 statistic p.value parame…¹ conf.…² conf.…³ method alter…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 0.444 0.627 46.9 7.34e-12 1 -0.236 -0.131 2-sam… two.si…
## # … with abbreviated variable names ¹parameter, ²conf.low, ³conf.high,
## # ⁴alternativek.gcond_GK_v_rep <- v.chi.sq(x2 = prop.test(t(k.g_table_rep[2:1, c(1,3)]))$statistic,
n = sum(t(k.g_table_rep[2:1, c(1,3)])),
r = 2, c = 2)
k.gcond_GK_v_rep## $v
## X-squared
## 0.1822556
##
## $vlow
## [1] 0.1328066
##
## $vhigh
## [1] 0.2359011
##
## $n
## [1] 1413
##
## $df
## [1] 1
##
## $x2
## X-squared
## 46.93575
##
## $p
## X-squared
## 0.00000000000733525
##
## $estimate
## [1] "$V$ = .18, 95\\% CI [.13, .24]"
##
## $statistic
## [1] "$\\chi^2$(1) = 46.94, $p$ < .001"k.gcond_GI_rep$estimate1## [1] 0.4439528k.gcond_GI_rep$estimate2## [1] 0.2486111k.gcond_GK_rep$estimate1## [1] 0.4439528k.gcond_GK_rep$estimate2## [1] 0.6272109Emma
- Overall chi-square is significant with a smaller effect size than Darrel or Gerald.
- Gettier versus Ignorance shows the same pattern of effects with approximately half of the effect size.
- Gettier versus Knowledge shows the same pattern of effects with approximately half of the effect size.
View full results
emma_only <- chisq.test(emma_DF$cond, emma_DF$know_vas_combined)
MOTE_emma <- v.chi.sq(
x2 = emma_only$statistic,
n = sum(emma_only$observed),
r = 2,
c = 3,
a = .05
)
emma_only##
## Pearson's Chi-squared test
##
## data: emma_DF$cond and emma_DF$know_vas_combined
## X-squared = 110.04, df = 2, p-value < 2.2e-16MOTE_emma## $v
## X-squared
## 0.2222373
##
## $vlow
## [1] 0.182073
##
## $vhigh
## [1] 0.264534
##
## $n
## [1] 2228
##
## $df
## [1] 2
##
## $x2
## X-squared
## 110.0397
##
## $p
## X-squared
## 1.274062e-24
##
## $estimate
## [1] "$V$ = .22, 95\\% CI [.18, .26]"
##
## $statistic
## [1] "$\\chi^2$(2) = 110.04, $p$ < .001"k.e_table_rep <- table(emma_DF$know_vas_combined, emma_DF$cond)
k.e_table_rep##
## Gettier Ignorance Knowledge
## 1 554 664 447
## 2 164 120 279k.econd_GI_rep <- prop.test(t(k.e_table_rep[2:1, 1:2])) %>%
tidy()
k.econd_GI_rep## # A tibble: 1 × 9
## estimate1 estimate2 statistic p.value parame…¹ conf.…² conf.…³ method alter…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 0.228 0.153 13.4 0.000253 1 0.0343 0.116 2-sam… two.si…
## # … with abbreviated variable names ¹parameter, ²conf.low, ³conf.high,
## # ⁴alternativek.econd_GI_v_rep <- v.chi.sq(x2 = prop.test(t(k.e_table_rep[2:1, 1:2]))$statistic,
n = sum(t(k.e_table_rep[2:1, 1:2])),
r = 2, c = 2)
k.econd_GI_v_rep## $v
## X-squared
## 0.09442094
##
## $vlow
## [1] 0.05087707
##
## $vhigh
## [1] 0.1472713
##
## $n
## [1] 1502
##
## $df
## [1] 1
##
## $x2
## X-squared
## 13.3908
##
## $p
## X-squared
## 0.0002528613
##
## $estimate
## [1] "$V$ = .09, 95\\% CI [.05, .15]"
##
## $statistic
## [1] "$\\chi^2$(1) = 13.39, $p$ < .001"k.econd_GK_rep <- prop.test(t(k.e_table_rep[2:1, c(1,3)])) %>%
tidy()
k.econd_GK_rep## # A tibble: 1 × 9
## estimate1 estimate2 statistic p.value parame…¹ conf.…² conf.…³ method alter…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 0.228 0.384 40.5 1.95e-10 1 -0.204 -0.108 2-sam… two.si…
## # … with abbreviated variable names ¹parameter, ²conf.low, ³conf.high,
## # ⁴alternativek.econd_GK_v_rep <- v.chi.sq(x2 = prop.test(t(k.e_table_rep[2:1, c(1,3)]))$statistic,
n = sum(t(k.e_table_rep[2:1, c(1,3)])),
r = 2, c = 2)
k.econd_GK_v_rep## $v
## X-squared
## 0.16751
##
## $vlow
## [1] 0.1188812
##
## $vhigh
## [1] 0.2206628
##
## $n
## [1] 1444
##
## $df
## [1] 1
##
## $x2
## X-squared
## 40.51804
##
## $p
## X-squared
## 0.000000000194809
##
## $estimate
## [1] "$V$ = .17, 95\\% CI [.12, .22]"
##
## $statistic
## [1] "$\\chi^2$(1) = 40.52, $p$ < .001"k.econd_GI_rep$estimate1## [1] 0.2284123k.econd_GI_rep$estimate2## [1] 0.1530612k.econd_GK_rep$estimate1## [1] 0.2284123k.econd_GK_rep$estimate2## [1] 0.3842975Update Table 6/Figure 3
log_exclude[[7]]@frame %>%
mutate(know_vas_combined = factor(know_vas_combined,
levels = c(0,1),
labels = c("Believes", "Knows"))) %>%
group_by(know_vas_combined, cond, vignette) %>%
summarize(frequency = n()) %>%
arrange(desc(cond)) %>%
pivot_wider(id_cols = c(know_vas_combined, vignette), names_from = cond, values_from = frequency) %>%
ungroup() ## # A tibble: 6 × 5
## know_vas_combined vignette Knowledge Ignorance Gettier
## <fct> <fct> <int> <int> <int>
## 1 Believes Darrel 454 1170 615
## 2 Believes Emma 993 1408 1266
## 3 Believes Gerald 558 1255 757
## 4 Knows Darrel 1126 353 923
## 5 Knows Emma 531 164 276
## 6 Knows Gerald 957 321 768Just a Midpoint Split
- The first results presented in each section represent the midpoint split, while the second results indicate the information presented in the manuscript.
# data prep
exclude_DF <- full_long %>%
filter(total_exclusion < 1)
# knowledge recode
exclude_DF$know_vas_binned <- exclude_DF$know_vas
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned <= 50] <- 2
exclude_DF$know_vas_binned[exclude_DF$know_vas_binned >= 50] <- 1
exclude_DF$know_vas_combined <- ifelse(is.na(exclude_DF$know_vas_binned),
exclude_DF$know_bin,
exclude_DF$know_vas_binned)
exclude_DF$know_vas_combined <- 3 - exclude_DF$know_vas_combined
# reason recode
exclude_DF$reason_vas_binned <- exclude_DF$reason_vas
exclude_DF$reason_vas_binned[exclude_DF$reason_vas_binned <= 50] <- 2
exclude_DF$reason_vas_binned[exclude_DF$reason_vas_binned >= 50] <- 1
exclude_DF$reason_vas_combined <- ifelse(is.na(exclude_DF$reason_vas_binned),
exclude_DF$reason_bin,
exclude_DF$reason_vas_binned)
exclude_DF$reason_vas_combined <- 3 - exclude_DF$reason_vas_combined
# luck recode
exclude_DF$luck_vas_binned <- exclude_DF$luck_vas
exclude_DF$luck_vas_binned[exclude_DF$luck_vas_binned <= 50] <- 2
exclude_DF$luck_vas_binned[exclude_DF$luck_vas_binned >= 50] <- 1
exclude_DF$luck_vas_combined <- ifelse(is.na(exclude_DF$luck_vas_binned),
exclude_DF$luck_bin,
exclude_DF$luck_vas_binned)
exclude_DF$luck_vas_combined <- 3 - exclude_DF$luck_vas_combined
# for luck analyses people should be excluded if they get the answer wrong
exclude_DF$luck_correct <- FALSE
exclude_DF$ri_wr <- factor(exclude_DF$ri_wr,
levels = c(1,2),
labels = c("Right", "Wrong"))
exclude_DF$luck_correct[exclude_DF$cond == "Ignorance" & exclude_DF$ri_wr == "Wrong"] <- TRUE
exclude_DF$luck_correct[exclude_DF$cond != "Ignorance" & exclude_DF$ri_wr == "Right"] <- TRUE
table(exclude_DF$luck_correct)##
## FALSE TRUE
## 952 13526# fix other variables
exclude_DF$gender2 <- factor(exclude_DF$gender,
levels = c("female", "male"))
# subset the wrong answers
exclude_luck <- subset(exclude_DF, luck_correct)Knowledge
Condition
- Results from coefficients are within a confidence interval of each other.
View full results
exclude_DF$know_vas_combined <- exclude_DF$know_vas_combined - 1
k.cond.exclude <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.cond.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 16394.9 16470.6 -8187.5 16374.9 14232
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7439 -0.6376 -0.4957 0.7857 3.7587
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 8.481e-79 9.209e-40
## id:vignette (Intercept) 2.500e-13 5.000e-07
## vignette (Intercept) 4.215e-01 6.492e-01
## Number of obs: 14242, groups:
## person_code:(id:vignette), 14242; id:vignette, 14242; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.199080 0.392708 -0.507 0.6122
## compYes 0.021265 0.043830 0.485 0.6276
## age 0.003641 0.001969 1.850 0.0643 .
## gender2male -0.090075 0.041913 -2.149 0.0316 *
## education -0.016465 0.007417 -2.220 0.0264 *
## condIgnorance -1.272714 0.049373 -25.778 <2e-16 ***
## condKnowledge 0.609300 0.043673 13.952 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.075
## age -0.108 0.193
## gender2male -0.029 0.014 -0.090
## education -0.238 -0.119 -0.115 0.032
## condIgnornc -0.049 0.001 -0.007 0.012 0.006
## condKnowldg -0.055 0.002 0.003 -0.005 -0.005 0.431
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[6]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15872.0 15947.4 -7926.0 15852.0 13885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7728 -0.6220 -0.4902 0.7726 3.8696
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000 0.0000
## id:vignette (Intercept) 0.0000 0.0000
## vignette (Intercept) 0.4474 0.6689
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.183810 0.404093 -0.455 0.6492
## compYes 0.019364 0.044633 0.434 0.6644
## age 0.003733 0.001997 1.870 0.0615 .
## gender2male -0.084539 0.042604 -1.984 0.0472 *
## education -0.017069 0.007528 -2.267 0.0234 *
## condIgnorance -1.313222 0.050238 -26.140 <2e-16 ***
## condKnowledge 0.611665 0.044435 13.765 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.075
## age -0.107 0.197
## gender2male -0.030 0.014 -0.090
## education -0.235 -0.118 -0.115 0.035
## condIgnornc -0.049 0.001 -0.007 0.011 0.008
## condKnowldg -0.054 0.001 0.002 -0.006 -0.005 0.431
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Vignette
- Results from coefficients are within a confidence interval of each other.
View full results
k.vignette.exclude <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*vignette,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.vignette.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 16329.3 16450.3 -8148.6 16297.3 14226
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7502 -0.5857 -0.4527 0.7933 3.0576
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 2.500e-13 0.0000005000000000
## id:vignette (Intercept) 6.091e-11 0.0000078046824951
## vignette (Intercept) 2.108e-26 0.0000000000001452
## Number of obs: 14242, groups:
## person_code:(id:vignette), 14242; id:vignette, 14242; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.523405 0.124250 4.213 0.0000252558666502
## compYes 0.018982 0.044031 0.431 0.666390
## age 0.003712 0.001975 1.879 0.060239
## gender2male -0.093158 0.042094 -2.213 0.026891
## education -0.015929 0.007448 -2.139 0.032465
## condIgnorance -1.567344 0.078867 -19.873 < 2e-16
## condKnowledge 0.498677 0.074927 6.656 0.0000000000282329
## vignetteEmma -1.889345 0.082955 -22.776 < 2e-16
## vignetteGerald -0.388073 0.071771 -5.407 0.0000000640507748
## condIgnorance:vignetteEmma 0.969825 0.130275 7.444 0.0000000000000974
## condKnowledge:vignetteEmma 0.400922 0.112501 3.564 0.000366
## condIgnorance:vignetteGerald 0.229634 0.111994 2.050 0.040325
## condKnowledge:vignetteGerald 0.022343 0.104374 0.214 0.830495
##
## (Intercept) ***
## compYes
## age .
## gender2male *
## education *
## condIgnorance ***
## condKnowledge ***
## vignetteEmma ***
## vignetteGerald ***
## condIgnorance:vignetteEmma ***
## condKnowledge:vignetteEmma ***
## condIgnorance:vignetteGerald *
## condKnowledge:vignetteGerald
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[7]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15807.7 15928.3 -7887.8 15775.7 13879
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7804 -0.5717 -0.4484 0.7819 3.1287
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000172891 0.0000041580
## id:vignette (Intercept) 0.0000000000007192 0.0000008481
## vignette (Intercept) 0.0000000000000000 0.0000000000
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.555020 0.126409 4.391 0.000011300772713 ***
## compYes 0.016780 0.044844 0.374 0.708268
## age 0.003778 0.002004 1.886 0.059360 .
## gender2male -0.087674 0.042794 -2.049 0.040489 *
## education -0.016600 0.007561 -2.196 0.028122 *
## condIgnorance -1.604292 0.080023 -20.048 < 2e-16 ***
## condKnowledge 0.503172 0.076198 6.603 0.000000000040161 ***
## vignetteEmma -1.930434 0.084439 -22.862 < 2e-16 ***
## vignetteGerald -0.390320 0.073064 -5.342 0.000000091833949 ***
## condIgnorance:vignetteEmma 0.977499 0.132791 7.361 0.000000000000182 ***
## condKnowledge:vignetteEmma 0.395026 0.114528 3.449 0.000562 ***
## condIgnorance:vignetteGerald 0.223192 0.113776 1.962 0.049800 *
## condKnowledge:vignetteGerald 0.021780 0.106168 0.205 0.837455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Turk
- Results from coefficients are within a confidence interval of each other.
View full results
k.turk.exclude <- glmer(know_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*turk,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(k.turk.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 16374.2 16472.5 -8174.1 16348.2 14229
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0596 -0.6286 -0.4915 0.7954 3.8176
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000 0.0000
## id:vignette (Intercept) 0.0000 0.0000
## vignette (Intercept) 0.4234 0.6507
## Number of obs: 14242, groups:
## person_code:(id:vignette), 14242; id:vignette, 14242; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0903101 0.3944471 -0.229 0.81890
## compYes -0.0310858 0.0455190 -0.683 0.49466
## age -0.0002424 0.0021899 -0.111 0.91185
## gender2male -0.1159354 0.0424451 -2.731 0.00631 **
## education -0.0157819 0.0074212 -2.127 0.03345 *
## condIgnorance -1.2512205 0.0513191 -24.381 < 2e-16 ***
## condKnowledge 0.5906307 0.0453756 13.016 < 2e-16 ***
## turkTRUE 0.3158457 0.1224020 2.580 0.00987 **
## condIgnorance:turkTRUE -0.2909027 0.1875978 -1.551 0.12098
## condKnowledge:turkTRUE 0.2795678 0.1689012 1.655 0.09788 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.090
## age -0.125 0.285
## gender2male -0.038 0.053 -0.021
## education -0.236 -0.120 -0.113 0.029
## condIgnornc -0.052 0.005 0.000 0.012 0.006
## condKnowldg -0.056 -0.001 -0.002 -0.006 -0.005 0.434
## turkTRUE 0.031 -0.184 -0.286 -0.098 0.014 0.161 0.186
## cndIgn:TRUE 0.014 -0.001 0.001 0.005 -0.001 -0.271 -0.121 -0.586
## cndKnw:TRUE 0.015 0.000 0.002 0.001 0.000 -0.119 -0.266 -0.650 0.424
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[8]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: know_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15850.2 15948.2 -7912.1 15824.2 13882
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1117 -0.6244 -0.4852 0.7822 3.9470
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0000000000002108 0.0000004591
## id:vignette (Intercept) 0.0000000000024070 0.0000015515
## vignette (Intercept) 0.4494139896495767 0.6703834646
## Number of obs: 13895, groups:
## person_code:(id:vignette), 13895; id:vignette, 13895; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0743861 0.4058531 -0.183 0.85458
## compYes -0.0330383 0.0463320 -0.713 0.47580
## age -0.0001138 0.0022169 -0.051 0.95906
## gender2male -0.1113136 0.0431663 -2.579 0.00992 **
## education -0.0164575 0.0075305 -2.185 0.02886 *
## condIgnorance -1.2894721 0.0521660 -24.719 < 2e-16 ***
## condKnowledge 0.5914056 0.0461318 12.820 < 2e-16 ***
## turkTRUE 0.3226120 0.1260957 2.558 0.01051 *
## condIgnorance:turkTRUE -0.3324922 0.1930846 -1.722 0.08507 .
## condKnowledge:turkTRUE 0.2986885 0.1735316 1.721 0.08521 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.090
## age -0.124 0.288
## gender2male -0.039 0.053 -0.021
## education -0.233 -0.119 -0.112 0.032
## condIgnornc -0.052 0.006 0.001 0.011 0.007
## condKnowldg -0.055 -0.001 -0.002 -0.006 -0.005 0.433
## turkTRUE 0.031 -0.182 -0.282 -0.098 0.011 0.158 0.184
## cndIgn:TRUE 0.013 -0.001 0.001 0.007 0.001 -0.268 -0.119 -0.588
## cndKnw:TRUE 0.014 0.000 0.002 -0.001 0.002 -0.118 -0.263 -0.654 0.427
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Reasonable
Condition
- Results from coefficients are within a confidence interval of each other.
View full results
exclude_DF$reason_vas_combined <- exclude_DF$reason_vas_combined - 1
r.cond.exclude <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.cond.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7618.2 7693.8 -3799.1 7598.2 14238
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1506 0.2213 0.2683 0.3204 0.5695
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.006747443567930 0.082142824
## id:vignette (Intercept) 0.000000000007386 0.000002718
## vignette (Intercept) 0.065531753404023 0.255991706
## Number of obs: 14248, groups:
## person_code:(id:vignette), 14248; id:vignette, 14248; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.784498 0.236073 7.559 0.0000000000000406 ***
## compYes 0.231500 0.069489 3.331 0.000864 ***
## age -0.003719 0.003117 -1.193 0.232864
## gender2male -0.198665 0.067234 -2.955 0.003128 **
## education 0.052064 0.011527 4.516 0.0000062871224868 ***
## condIgnorance -0.346888 0.072294 -4.798 0.0000016002058570 ***
## condKnowledge 0.449632 0.085877 5.236 0.0000001642958072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.203
## age -0.301 0.200
## gender2male -0.086 0.014 -0.093
## education -0.611 -0.100 -0.097 0.030
## condIgnornc -0.174 -0.005 0.001 0.001 -0.001
## condKnowldg -0.151 0.001 0.001 -0.002 0.006 0.480
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[14]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7047.1 7122.6 -3513.6 7027.1 13964
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6491 0.2093 0.2544 0.3073 0.5823
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.001461 0.03822
## id:vignette (Intercept) 0.007296 0.08542
## vignette (Intercept) 0.076656 0.27687
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.839819 0.248928 7.391 0.000000000000146 ***
## compYes 0.251565 0.072504 3.470 0.000521 ***
## age -0.004968 0.003209 -1.548 0.121581
## gender2male -0.181153 0.070632 -2.565 0.010326 *
## education 0.057713 0.011926 4.839 0.000001303215506 ***
## condIgnorance -0.400271 0.075940 -5.271 0.000000135772713 ***
## condKnowledge 0.425613 0.090671 4.694 0.000002678579960 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.202
## age -0.297 0.201
## gender2male -0.085 0.014 -0.095
## education -0.599 -0.098 -0.097 0.030
## condIgnornc -0.178 -0.005 0.002 0.002 0.001
## condKnowldg -0.154 0.003 0.003 -0.002 0.007 0.487Vignette
- Results from coefficients are within a confidence interval of each other.
View full results
r.vignette.exclude <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*vignette,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.vignette.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * vignette
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7594.4 7715.4 -3781.2 7562.4 14232
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.9075 0.2153 0.2680 0.3298 0.5401
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.001283669 0.035828
## id:vignette (Intercept) 0.000001523 0.001234
## vignette (Intercept) 0.000000000 0.000000
## Number of obs: 14248, groups:
## person_code:(id:vignette), 14248; id:vignette, 14248; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.307115 0.211421 10.912 < 2e-16 ***
## compYes 0.228689 0.069547 3.288 0.00101 **
## age -0.003725 0.003127 -1.191 0.23355
## gender2male -0.199997 0.067279 -2.973 0.00295 **
## education 0.052870 0.011533 4.584 0.000004560255809 ***
## condIgnorance -0.769447 0.148472 -5.182 0.000000219011965 ***
## condKnowledge 0.449781 0.189475 2.374 0.01760 *
## vignetteEmma -1.025109 0.143274 -7.155 0.000000000000837 ***
## vignetteGerald -0.423609 0.156103 -2.714 0.00665 **
## condIgnorance:vignetteEmma 0.726863 0.184537 3.939 0.000081875177357 ***
## condKnowledge:vignetteEmma 0.231065 0.229668 1.006 0.31438
## condIgnorance:vignetteGerald 0.337224 0.197176 1.710 0.08722 .
## condKnowledge:vignetteGerald -0.304036 0.239462 -1.270 0.20421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[15]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * vignette
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7025.8 7146.5 -3496.9 6993.8 13958
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4722 0.2020 0.2562 0.3173 0.5561
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.000000 0.00000
## id:vignette (Intercept) 0.002124 0.04609
## vignette (Intercept) 0.000000 0.00000
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.425110 0.222940 10.878 < 2e-16 ***
## compYes 0.248903 0.072555 3.431 0.000602 ***
## age -0.005006 0.003218 -1.556 0.119764
## gender2male -0.181886 0.070666 -2.574 0.010056 *
## education 0.058594 0.011936 4.909 0.0000009160223 ***
## condIgnorance -0.863705 0.160582 -5.379 0.0000000750731 ***
## condKnowledge 0.390875 0.204642 1.910 0.056127 .
## vignetteEmma -1.099565 0.155615 -7.066 0.0000000000016 ***
## vignetteGerald -0.522306 0.168276 -3.104 0.001910 **
## condIgnorance:vignetteEmma 0.741195 0.196886 3.765 0.000167 ***
## condKnowledge:vignetteEmma 0.256617 0.245839 1.044 0.296559
## condIgnorance:vignetteGerald 0.424379 0.210062 2.020 0.043357 *
## condKnowledge:vignetteGerald -0.237181 0.255828 -0.927 0.353869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Turk
- Results from coefficients are within a confidence interval of each other.
View full results
r.turk.exclude <- glmer(reason_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*turk,
data = exclude_DF,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(r.turk.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: exclude_DF
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7595.4 7693.8 -3784.7 7569.4 14235
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.1773 0.2174 0.2682 0.3211 0.6700
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0017023 0.04126
## id:vignette (Intercept) 0.0002381 0.01543
## vignette (Intercept) 0.0656375 0.25620
## Number of obs: 14248, groups:
## person_code:(id:vignette), 14248; id:vignette, 14248; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.984053 0.238726 8.311 < 2e-16 ***
## compYes 0.128874 0.072531 1.777 0.075599 .
## age -0.010321 0.003297 -3.131 0.001744 **
## gender2male -0.243720 0.067707 -3.600 0.000319 ***
## education 0.051489 0.011357 4.533 0.0000058015 ***
## condIgnorance -0.324940 0.073919 -4.396 0.0000110332 ***
## condKnowledge 0.468246 0.087756 5.336 0.0000000951 ***
## turkTRUE 1.163850 0.317981 3.660 0.000252 ***
## condIgnorance:turkTRUE -0.559833 0.375913 -1.489 0.136418
## condKnowledge:turkTRUE -0.468951 0.442526 -1.060 0.289274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.252
## age -0.343 0.310
## gender2male -0.111 0.053 -0.035
## education -0.596 -0.096 -0.089 0.034
## condIgnornc -0.175 -0.003 0.004 0.003 0.000
## condKnowldg -0.151 0.001 0.000 -0.003 0.006 0.475
## turkTRUE 0.050 -0.124 -0.173 -0.057 -0.003 0.131 0.111
## cndIgn:TRUE 0.035 0.001 0.000 -0.002 0.000 -0.197 -0.093 -0.813
## cndKnw:TRUE 0.030 0.000 0.001 0.001 -0.002 -0.094 -0.198 -0.691 0.584summary(log_exclude[[16]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7017.4 7115.5 -3495.7 6991.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5047 0.2054 0.2546 0.3087 0.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0023770476 0.0487550
## id:vignette (Intercept) 0.0000001472 0.0003837
## vignette (Intercept) 0.0768855931 0.2772825
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.073866 0.251554 8.244 < 2e-16 ***
## compYes 0.131776 0.075708 1.741 0.081757 .
## age -0.012466 0.003376 -3.692 0.000222 ***
## gender2male -0.232380 0.071089 -3.269 0.001080 **
## education 0.056843 0.011725 4.848 0.00000125 ***
## condIgnorance -0.378343 0.077451 -4.885 0.00000103 ***
## condKnowledge 0.440103 0.092314 4.767 0.00000187 ***
## turkTRUE 1.385038 0.368873 3.755 0.000173 ***
## condIgnorance:turkTRUE -0.655384 0.427927 -1.532 0.125638
## condKnowledge:turkTRUE -0.443704 0.514800 -0.862 0.388745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.251
## age -0.338 0.315
## gender2male -0.109 0.053 -0.039
## education -0.583 -0.094 -0.088 0.035
## condIgnornc -0.180 -0.003 0.005 0.004 0.001
## condKnowldg -0.154 0.002 0.001 -0.003 0.007 0.484
## turkTRUE 0.044 -0.113 -0.156 -0.050 -0.003 0.120 0.101
## cndIgn:TRUE 0.032 0.001 0.001 -0.002 -0.001 -0.181 -0.088 -0.835
## cndKnw:TRUE 0.027 0.000 0.001 0.001 -0.002 -0.087 -0.179 -0.694 0.598Luck
Condition
- Results from coefficients are within a confidence interval of each other.
View full results
exclude_luck$luck_vas_combined <- exclude_luck$luck_vas_combined - 1
l.cond.exclude <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond,
data = exclude_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.cond.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: exclude_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 16878.5 16953.5 -8429.3 16858.5 13295
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1704 -0.9144 0.4729 0.8213 2.1331
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0008215 0.02866
## id:vignette (Intercept) 0.0099704 0.09985
## vignette (Intercept) 0.3395371 0.58270
## Number of obs: 13305, groups:
## person_code:(id:vignette), 13305; id:vignette, 13305; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.222316 0.355676 0.625 0.5319
## compYes 0.169379 0.042610 3.975 0.0000703 ***
## age -0.004508 0.001915 -2.355 0.0185 *
## gender2male -0.028417 0.040747 -0.697 0.4855
## education 0.031464 0.007282 4.321 0.0000155 ***
## condIgnorance -0.984890 0.045897 -21.459 < 2e-16 ***
## condKnowledge -0.894174 0.045646 -19.589 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.080
## age -0.117 0.186
## gender2male -0.030 0.013 -0.089
## education -0.259 -0.114 -0.110 0.025
## condIgnornc -0.065 -0.009 0.016 0.005 -0.013
## condKnowldg -0.063 -0.012 0.009 0.003 -0.015 0.525summary(log_exclude[[22]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15896.2 15970.6 -7938.1 15876.2 12630
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1575 -0.8039 -0.4574 0.9076 2.2438
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.009841 0.09920
## id:vignette (Intercept) 0.004391 0.06626
## vignette (Intercept) 0.373469 0.61112
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.246316 0.372248 -0.662 0.5082
## compYes -0.180694 0.043976 -4.109 0.0000398 ***
## age 0.004153 0.001967 2.111 0.0348 *
## gender2male 0.007267 0.042185 0.172 0.8632
## education -0.031096 0.007485 -4.155 0.0000326 ***
## condIgnorance 1.027734 0.047414 21.676 < 2e-16 ***
## condKnowledge 0.935679 0.047106 19.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn
## compYes -0.079
## age -0.116 0.191
## gender2male -0.029 0.014 -0.086
## education -0.253 -0.116 -0.109 0.023
## condIgnornc -0.064 -0.007 0.018 0.002 -0.014
## condKnowldg -0.062 -0.012 0.009 -0.001 -0.017 0.525Vignette
- Results from coefficients are within a confidence interval of each other.
View full results
l.vignette.exclude <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*vignette,
data = exclude_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.vignette.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: exclude_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 16430.9 16550.9 -8199.5 16398.9 13289
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4902 -0.8498 0.2947 1.0165 2.1699
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.00234642 0.048440
## id:vignette (Intercept) 0.00001009 0.003176
## vignette (Intercept) 0.00000000 0.000000
## Number of obs: 13305, groups:
## person_code:(id:vignette), 13305; id:vignette, 13305; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.878536 0.125085 -7.024 0.000000000002163 ***
## compYes 0.185387 0.043311 4.280 0.000018659951524 ***
## age -0.004608 0.001950 -2.363 0.0181 *
## gender2male -0.016309 0.041291 -0.395 0.6929
## education 0.031213 0.007401 4.217 0.000024709433765 ***
## condIgnorance -0.064460 0.076051 -0.848 0.3967
## condKnowledge -0.713049 0.080347 -8.875 < 2e-16 ***
## vignetteEmma 2.720971 0.104934 25.930 < 2e-16 ***
## vignetteGerald 1.016167 0.076374 13.305 < 2e-16 ***
## condIgnorance:vignetteEmma -2.444410 0.128950 -18.956 < 2e-16 ***
## condKnowledge:vignetteEmma -0.988465 0.132107 -7.482 0.000000000000073 ***
## condIgnorance:vignetteGerald -0.695187 0.107310 -6.478 0.000000000092751 ***
## condKnowledge:vignetteGerald -0.048157 0.110705 -0.435 0.6636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(log_exclude[[23]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * vignette
## Data: final_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 15458.4 15577.5 -7713.2 15426.4 12624
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1894 -1.0118 -0.2784 0.8435 3.7019
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0004629 0.02151
## id:vignette (Intercept) 0.0015660 0.03957
## vignette (Intercept) 0.0000000 0.00000
## Number of obs: 12640, groups:
## person_code:(id:vignette), 12640; id:vignette, 12640; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.907304 0.128577 7.056 0.0000000000017077
## compYes -0.195308 0.044696 -4.370 0.0000124396016865
## age 0.004152 0.002002 2.074 0.0381
## gender2male -0.004211 0.042725 -0.099 0.9215
## education -0.031082 0.007607 -4.086 0.0000439445273061
## condIgnorance 0.083929 0.078418 1.070 0.2845
## condKnowledge 0.723553 0.082462 8.774 < 2e-16
## vignetteEmma -2.851015 0.110619 -25.773 < 2e-16
## vignetteGerald -1.070444 0.078819 -13.581 < 2e-16
## condIgnorance:vignetteEmma 2.527798 0.135060 18.716 < 2e-16
## condKnowledge:vignetteEmma 1.067342 0.138234 7.721 0.0000000000000115
## condIgnorance:vignetteGerald 0.723264 0.110503 6.545 0.0000000000594091
## condKnowledge:vignetteGerald 0.094904 0.113794 0.834 0.4043
##
## (Intercept) ***
## compYes ***
## age *
## gender2male
## education ***
## condIgnorance
## condKnowledge ***
## vignetteEmma ***
## vignetteGerald ***
## condIgnorance:vignetteEmma ***
## condKnowledge:vignetteEmma ***
## condIgnorance:vignetteGerald ***
## condKnowledge:vignetteGerald
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Turk
- Results from coefficients are within a confidence interval of each other.
View full results
l.turk.exclude <- glmer(luck_vas_combined ~ (1|vignette/id/person_code) +
comp + age + gender2 + education +
cond*turk,
data = exclude_luck,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 0)
summary(l.turk.exclude)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: luck_vas_combined ~ (1 | vignette/id/person_code) + comp + age +
## gender2 + education + cond * turk
## Data: exclude_luck
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 16860.1 16957.6 -8417.1 16834.1 13292
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3274 -0.9133 0.4662 0.8292 2.2508
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0093557 0.09672
## id:vignette (Intercept) 0.0008057 0.02838
## vignette (Intercept) 0.3393677 0.58255
## Number of obs: 13305, groups:
## person_code:(id:vignette), 13305; id:vignette, 13305; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.333303 0.356602 0.935 0.34996
## compYes 0.116316 0.044350 2.623 0.00872 **
## age -0.008610 0.002141 -4.022 0.00005772 ***
## gender2male -0.052788 0.041196 -1.281 0.20006
## education 0.032480 0.007294 4.453 0.00000848 ***
## condIgnorance -1.004532 0.047699 -21.060 < 2e-16 ***
## condKnowledge -0.884074 0.047447 -18.633 < 2e-16 ***
## turkTRUE 0.311124 0.132378 2.350 0.01876 *
## condIgnorance:turkTRUE 0.261993 0.175805 1.490 0.13616
## condKnowledge:turkTRUE -0.143054 0.173024 -0.827 0.40836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.096
## age -0.136 0.281
## gender2male -0.039 0.050 -0.018
## education -0.256 -0.118 -0.113 0.020
## condIgnornc -0.068 -0.006 0.020 0.005 -0.014
## condKnowldg -0.066 -0.009 0.012 0.004 -0.015 0.523
## turkTRUE 0.026 -0.170 -0.267 -0.086 0.018 0.179 0.182
## cndIgn:TRUE 0.019 0.001 -0.008 0.004 0.003 -0.268 -0.139 -0.685
## cndKnw:TRUE 0.018 0.005 0.005 0.000 0.000 -0.140 -0.272 -0.699 0.525summary(log_exclude[[24]])## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 0) [glmerMod]
## Family: binomial ( logit )
## Formula: reason_vas_combined ~ (1 | vignette/id/person_code) + comp +
## age + gender2 + education + cond * turk
## Data: final_long
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 7017.4 7115.5 -3495.7 6991.4 13961
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5047 0.2054 0.2546 0.3087 0.6997
##
## Random effects:
## Groups Name Variance Std.Dev.
## person_code:(id:vignette) (Intercept) 0.0023770476 0.0487550
## id:vignette (Intercept) 0.0000001472 0.0003837
## vignette (Intercept) 0.0768855931 0.2772825
## Number of obs: 13974, groups:
## person_code:(id:vignette), 13974; id:vignette, 13974; vignette, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.073866 0.251554 8.244 < 2e-16 ***
## compYes 0.131776 0.075708 1.741 0.081757 .
## age -0.012466 0.003376 -3.692 0.000222 ***
## gender2male -0.232380 0.071089 -3.269 0.001080 **
## education 0.056843 0.011725 4.848 0.00000125 ***
## condIgnorance -0.378343 0.077451 -4.885 0.00000103 ***
## condKnowledge 0.440103 0.092314 4.767 0.00000187 ***
## turkTRUE 1.385038 0.368873 3.755 0.000173 ***
## condIgnorance:turkTRUE -0.655384 0.427927 -1.532 0.125638
## condKnowledge:turkTRUE -0.443704 0.514800 -0.862 0.388745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) compYs age gndr2m eductn cndIgn cndKnw trTRUE cI:TRU
## compYes -0.251
## age -0.338 0.315
## gender2male -0.109 0.053 -0.039
## education -0.583 -0.094 -0.088 0.035
## condIgnornc -0.180 -0.003 0.005 0.004 0.001
## condKnowldg -0.154 0.002 0.001 -0.003 0.007 0.484
## turkTRUE 0.044 -0.113 -0.156 -0.050 -0.003 0.120 0.101
## cndIgn:TRUE 0.032 0.001 0.001 -0.002 -0.001 -0.181 -0.088 -0.835
## cndKnw:TRUE 0.027 0.000 0.001 0.001 -0.002 -0.087 -0.179 -0.694 0.598Graph of Area Representation
# convert country code to region code
# this will warn you about the ones that have multiple countries
# create a world map
world_map <- map_data(map = "world")
world_map$orig_region <- world_map$region
world_map$region <- iso.alpha(world_map$region, n = 3)
world_map <- subset(world_map, region != "ATA")
# summarize the same samples
country_summary <- full_long %>%
filter(!duplicated(full_long %>% select(id))) %>%
group_by(lab_country) %>%
summarize(n = n()) %>%
filter(!is.na(lab_country))
# make a map on a continuous scale
ggplot(country_summary) +
geom_map(aes(map_id = lab_country, fill = n), map = world_map) +
geom_polygon(data = world_map,
aes(x = long, y = lat, group = group),
colour = 'black', fill = NA) +
theme_void() +
scale_fill_distiller(name = "Sample Size",
palette = "Greys",
direction = 1,
na.value = "white") 
ggsave("figure/continuous_country.png")
# maybe try binning
country_summary$n_binned <- if_else(
country_summary$n > 1000, "1000+",
if_else(
country_summary$n < 1000 & country_summary$n >= 400, "400-999",
if_else(
country_summary$n < 400 & country_summary$n >= 100, "100-399",
"< 100"
)
)
)
country_summary$n_binned <- factor(country_summary$n_binned,
levels = c("< 100", "100-399",
"400-999", "1000+"))
# map of binned data
bin_country <- ggplot(country_summary) +
geom_map(aes(map_id = lab_country, fill = n_binned), map = world_map) +
geom_polygon(data = world_map,
aes(x = long, y = lat, group = group),
colour = 'black', fill = NA) +
theme_void() +
scale_fill_manual(name = "Sample Size",
values = c("#c8c8c8", "#969696", "#646464", "#323232"))
bin_country
ggsave("figure/binned_country.png", bin_country)
# tree map
country_summary$un_region_sub <- countrycode(
sourcevar = country_summary$lab_country,
origin = 'iso3c',
destination = 'un.regionsub.name'
)## Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: TWNcountry_summary$un_region <- countrycode(
sourcevar = country_summary$lab_country,
origin = 'iso3c',
destination = 'un.region.name'
)## Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: TWNcountry_summary$un_region[is.na(country_summary$un_region)] <- "Asia"
country_summary$un_region_sub[is.na(country_summary$un_region_sub)] <- "Eastern Asia"
tree <- ggplot(country_summary, aes(area = n, fill = n_binned,
label = lab_country, subgroup = un_region_sub)) +
geom_treemap() +
geom_treemap_subgroup_border(colour = "white", size = 5) +
# geom_treemap_subgroup_text(place = "top", grow = TRUE,
# alpha = 0.25, colour = "black",
# fontface = "italic") +
geom_treemap_text(colour = "white", place = "centre",
size = 15, grow = FALSE) +
scale_fill_manual(name = "Sample Size",
values = c("#c8c8c8", "#969696", "#646464", "#323232"))
# scale_fill_gradient(name = "Sample Size",
# low = "#c8c8c8",
# high = "#323232")
tree 
ggsave("figure/treemap.png",
tree)Forest Plot of Effects
Knowledge
table(final_long$lab_country, useNA = "ifany")##
## AUS AUT CAN CHE DEU GBR GRC HUN NOR NZL POL PRT ROU RUS SGP SVK
## 723 453 774 84 2316 396 156 1347 228 126 579 243 1113 297 156 315
## TUR TWN USA
## 231 267 4674unique_country <- unique(final_long$lab_country)
country_results <- list()
for (i in unique_country){
temp_data <- final_long %>%
filter(lab_country == i)
k_table <- table(temp_data$know_vas_combined, temp_data$cond)
tryCatch(k_GI <- v.chi.sq(x2 = prop.test(t(k_table[2:1, 1:2]))$statistic,
n = sum(t(k_table[2:1, 1:2])),
r = 2, c = 2),
warning = function(w) {
k_GI <<- v.chi.sq(x2 = prop.test(t(k_table[2:1, 1:2]))$statistic,
n = sum(t(k_table[2:1, 1:2])),
r = 2, c = 2)
k_GI$vlow <<- 0
}
)
tryCatch( k_GK <- v.chi.sq(x2 = prop.test(t(k_table[2:1, c(1,3)]))$statistic,
n = sum(t(k_table[2:1, c(1,3)])),
r = 2, c = 2),
warning = function(w) {
k_GK <<- v.chi.sq(x2 = prop.test(t(k_table[2:1, c(1,3)]))$statistic,
n = sum(t(k_table[2:1, c(1,3)])),
r = 2, c = 2)
k_GK$vlow <<- 0
}
)
country_results[[i]]$country <- i
country_results[[i]]$GI <- k_GI$v
country_results[[i]]$GI_low <- k_GI$vlow
country_results[[i]]$GI_high <- k_GI$vhigh
country_results[[i]]$GK <- k_GK$v
country_results[[i]]$GK_low <- k_GK$vlow
country_results[[i]]$GK_high <- k_GK$vhigh
country_results[[i]]$sample <- nrow(temp_data)
}## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).
## Warning: The size of the effect combined with the degrees of freedom is too
## small to determine a lower confidence limit for the 'alpha.lower' (or the
## (1/2)(1-'conf.level') symmetric) value specified (set to zero).country_DF <- bind_rows(country_results)
ggplot(country_DF, aes(country, GI)) +
theme_classic() +
geom_point(aes(size = sample)) +
geom_errorbar(aes(ymin = GI_low, ymax = GI_high)) +
ylab("Knowledge Cramer's V for Gettier-Ignorance") +
xlab("Geopolitical Region") +
coord_flip() +
theme(legend.position = "none")
ggplot(country_DF, aes(country, GK)) +
theme_classic() +
geom_point(aes(size = sample)) +
geom_errorbar(aes(ymin = GK_low, ymax = GK_high)) +
ylab("Knowledge Cramer's V for Gettier-Knowledge") +
xlab("Geopolitical Region") +
coord_flip() +
theme(legend.position = "none")